Summary
Conference Proceedings |
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Journal Articles |
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Editorials |
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Book Chapters |
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Recent Preprints |
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Patents |
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2023
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Point Cloud Registration based on Gaussian Mixtures and Pairwise Wasserstein Distances
S. Steuernagel, A. Kurda, and M. Baum
2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI), 2023.
BibTex
DOI
Source Code
@inproceedings{2023_Steuernagel,
author = {Steuernagel, Simon and Kurda, Aaron and Baum, Marcus},
booktitle = {2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI)},
title = {Point Cloud Registration based on Gaussian Mixtures and Pairwise Wasserstein Distances},
year = {2023},
month = nov,
publisher = {IEEE},
code = {https://github.com/Fusion-Goettingen/MFI_2023_Steuernagel_GWDICP},
doi = {10.1109/sdf-mfi59545.2023.10361440}
}
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Single-Frame Radar Odometry Incorporating Bearing Uncertainty
K. Thormann and M. Baum
2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI), 2023.
Best Paper Award, 2nd Runner Up
BibTex
DOI
Source Code
@inproceedings{2023_Thormann,
author = {Thormann, Kolja and Baum, Marcus},
booktitle = {2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI)},
title = {Single-Frame Radar Odometry Incorporating Bearing Uncertainty},
year = {2023},
month = nov,
publisher = {IEEE},
award = {Best Paper Award, 2nd Runner Up},
code = {https://github.com/Fusion-Goettingen/MFI_2023_Thormann_Radar_Odometry},
doi = {10.1109/sdf-mfi59545.2023.10361339}
}
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Evaluation Scores for Elliptic Extended Object Tracking Considering Diverse Object Sizes
S. Steuernagel, K. Thormann, and M. Baum
2023 26th International Conference on Information Fusion (FUSION), 2023.
BibTex
DOI
Source Code
@inproceedings{2023_Steuernagela,
author = {Steuernagel, Simon and Thormann, Kolja and Baum, Marcus},
booktitle = {2023 26th International Conference on Information Fusion (FUSION)},
title = {Evaluation Scores for Elliptic Extended Object Tracking Considering Diverse Object Sizes},
year = {2023},
month = jun,
publisher = {IEEE},
code = {https://github.com/Fusion-Goettingen/Fusion_2023_Steuernagel_EvaluationScores},
doi = {10.23919/fusion52260.2023.10224112}
}
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Improved Extended Object Tracking with Efficient Particle-based Orientation Estimation
S. Steuernagel, K. Thormann, and M. Baum
2023 26th International Conference on Information Fusion (FUSION), 2023.
BibTex
DOI
Source Code
@inproceedings{2023_Steuernagelb,
author = {Steuernagel, Simon and Thormann, Kolja and Baum, Marcus},
booktitle = {2023 26th International Conference on Information Fusion (FUSION)},
title = {Improved Extended Object Tracking with Efficient Particle-based Orientation Estimation},
year = {2023},
month = jun,
publisher = {IEEE},
code = {https://github.com/Fusion-Goettingen/Fusion_2023_Steuernagel_EOTParticleFilter},
doi = {10.23919/fusion52260.2023.10224128}
}
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Track-to-track Association based on Stochastic Optimization
L. M. Wolf, S. Steuernagel, K. Thormann, and M. Baum
2023 26th International Conference on Information Fusion (FUSION), 2023.
BibTex
DOI
Source Code
@inproceedings{2023_Wolf,
author = {Wolf, Laura M. and Steuernagel, Simon and Thormann, Kolja and Baum, Marcus},
booktitle = {2023 26th International Conference on Information Fusion (FUSION)},
title = {Track-to-track Association based on Stochastic Optimization},
year = {2023},
month = jun,
publisher = {IEEE},
code = {https://github.com/Fusion-Goettingen/Fusion_2023_Wolf_T2TA},
doi = {10.23919/fusion52260.2023.10224113}
}
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Multitarget–Multidetection Tracking Using the Kernel SME Filter
E. Ernst, F. Pfaff, M. Baum, and U. D. Hanebeck
2023 26th International Conference on Information Fusion (FUSION), 2023.
BibTex
DOI
@inproceedings{2023_Ernst,
author = {Ernst, Eugen and Pfaff, Florian and Baum, Marcus and Hanebeck, Uwe D.},
booktitle = {2023 26th International Conference on Information Fusion (FUSION)},
title = {Multitarget–Multidetection Tracking Using the Kernel SME Filter},
year = {2023},
month = jun,
publisher = {IEEE},
doi = {10.23919/fusion52260.2023.10224206}
}
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Adaptive Kalman Filter Tracking for Instantaneous Aircraft Flutter Monitoring
R. Volkmar, K. Thormann, K. Soal, Y. Govers, M. Böswald, and M. Baum
2023 26th International Conference on Information Fusion (FUSION), 2023.
BibTex
DOI
@inproceedings{2023_Volkmar,
author = {Volkmar, Robin and Thormann, Kolja and Soal, Keith and Govers, Yves and Böswald, Marc and Baum, Marcus},
booktitle = {2023 26th International Conference on Information Fusion (FUSION)},
title = {Adaptive Kalman Filter Tracking for Instantaneous Aircraft Flutter Monitoring},
year = {2023},
month = jun,
publisher = {IEEE},
doi = {10.23919/fusion52260.2023.10224091}
}
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The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking
E. Ernst, F. Pfaff, U. D. Hanebeck, and M. Baum
2023 American Control Conference (ACC), 2023.
Finalist Best Student Paper Award
BibTex
DOI
@inproceedings{2023_Ernsta,
author = {Ernst, Eugen and Pfaff, Florian and Hanebeck, Uwe D. and Baum, Marcus},
booktitle = {2023 American Control Conference (ACC)},
title = {The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking},
year = {2023},
month = may,
publisher = {IEEE},
award = {Finalist Best Student Paper Award},
doi = {10.23919/acc55779.2023.10155842}
}
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An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data
J. S. Fowdur, M. Baum, F. Heymann, and P. Banys
Remote Sensing, vol. 15, no. 10, May 2023.
BibTex
DOI
@article{2023_Fowdur,
author = {Fowdur, Jaya Shradha and Baum, Marcus and Heymann, Frank and Banys, Pawel},
journal = {Remote Sensing},
title = {An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data},
year = {2023},
issn = {2072-4292},
month = may,
number = {10},
pages = {2503},
volume = {15},
doi = {10.3390/rs15102503},
publisher = {MDPI AG}
}
2022
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CNN-based Shape Estimation for Extended Object Tracking using Point Cloud Measurements
S. Steuernagel, K. Thormann, and M. Baum
Proceedings of the 25th International Conference on Information Fusion (Fusion 2022), Linköping, Sweden, 2022.
BibTex
DOI
Source Code
@inproceedings{2022_Steuernagel,
author = {Steuernagel, Simon and Thormann, Kolja and Baum, Marcus},
booktitle = {Proceedings of the 25th International Conference on Information Fusion (Fusion 2022)},
title = {CNN-based Shape Estimation for Extended Object Tracking using Point Cloud Measurements},
year = {2022},
address = {Link\"oping, Sweden},
month = jul,
code = {https://github.com/Fusion-Goettingen/Fusion2022_Steuernagel_CNN-EOT},
doi = {10.23919/FUSION49751.2022.9841254}
}
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Track-to-Track Fusion for Elliptical Extended Targets Parameterized with Orientation and Semi-Axes Lengths
K. Thormann and M. Baum
Proceedings of the 25th International Conference on Information Fusion (Fusion 2022), Linköping, Sweden, 2022.
BibTex
DOI
Source Code
@inproceedings{2022_Fusion_Thormann,
author = {Thormann, Kolja and Baum, Marcus},
booktitle = {Proceedings of the 25th International Conference on Information Fusion (Fusion 2022)},
title = {Track-to-Track Fusion for Elliptical Extended Targets Parameterized with Orientation and Semi-Axes Lengths},
year = {2022},
address = {Link\"oping, Sweden},
month = jul,
code = {https://github.com/Fusion-Goettingen/Fusion_2022_Thormann_RED-IF},
doi = {10.23919/FUSION49751.2022.9841310}
}
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Deterministic Gaussian Filtering based on Herding
L. M. Wolf and M. Baum
Proceedings of the 25th International Conference on Information Fusion (Fusion 2022), Linköping, Sweden, 2022.
BibTex
DOI
Source Code
@inproceedings{2022_Wolf,
author = {Wolf, Laura M. and Baum, Marcus},
booktitle = {Proceedings of the 25th International Conference on Information Fusion (Fusion 2022)},
title = {Deterministic Gaussian Filtering based on Herding},
year = {2022},
address = {Link\"oping, Sweden},
month = jul,
code = {https://github.com/Fusion-Goettingen/Fusion_2022_Wolf_HKF},
doi = {10.23919/FUSION49751.2022.9841346}
}
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A Tutorial on Multiple Extended Object Tracking
K. Granström and M. Baum
TechRxiv, Feb. 2022.
Tutorial webpage
BibTex
PDF
DOI
@article{2022_Granstroem,
author = {Granstr\"om, Karl and Baum, Marcus},
journal = {TechRxiv},
title = {A Tutorial on Multiple Extended Object Tracking},
year = {2022},
month = feb,
archiveprefix = {Preprint},
doi = {10.36227/techrxiv.19115858.v1},
file = {pdf:http\://www.techrxiv.org/ndownloader/files/33962585/1},
url = {https://www.techrxiv.org/articles/preprint/A_Tutorial_on_Multiple_Extended_Object_Tracking/19115858/1},
webnote = { Tutorial webpage }
}
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ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures
K. A. Thormann, V. Tozzi, P. Starke, H. Bickeböller, M. Baum, and A. Rosenberger
BMC Bioinformatics, vol. 23, no. 1, 2022.
Abstract
BibTex
URL
Source Code
ImputAccur is a software tool to measure genotype-imputation accuracy. Imputation of untyped markers is a standard approach in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy for imputed genotypes is fundamental. Several accuracy measures have been proposed, but unfortunately, they are implemented on different platforms, which is impractical.
@article{2022_BMC_Thormann,
author = {Thormann, Kolja A. and Tozzi, Viola and Starke, Paula and Bickeb\"oller, Heike and Baum, Marcus and Rosenberger, Albert},
journal = {BMC Bioinformatics},
title = {ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures},
year = {2022},
issn = {1471-2105},
number = {1},
pages = {316},
volume = {23},
code = {https://gitlab.gwdg.de/kolja.thormann1/imputationquality.git},
refid = {Thormann2022},
url = {https://doi.org/10.1186/s12859-022-04863-z}
}
2021
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An Elliptical Principal Axes-based Model for Extended Target Tracking with Marine Radar Data
J. S. Fowdur, M. Baum, and F. Heymann
Proceedings of the 24th International Conference on Information Fusion (Fusion 2021), South Africa, 2021.
BibTex
@inproceedings{2021_Fowdura,
author = {Fowdur, Jaya Shradha and Baum, Marcus and Heymann, Frank},
booktitle = {Proceedings of the 24th International Conference on Information Fusion (Fusion 2021)},
title = {An Elliptical Principal Axes-based Model for Extended Target Tracking with Marine Radar Data},
year = {2021},
address = {South Africa},
month = nov
}
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Continuous Herded Gibbs Sampling
L. Wolf and M. Baum
Proceedings of the 24th International Conference on Information Fusion (Fusion 2021), South Africa, 2021.
BibTex
@inproceedings{2021_Wolf,
author = {Wolf, Laura and Baum, Marcus},
booktitle = {Proceedings of the 24th International Conference on Information Fusion (Fusion 2021)},
title = {Continuous Herded {G}ibbs Sampling},
year = {2021},
address = {South Africa},
month = nov
}
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Incorporating Range-Rate Measurements in EKF-based Elliptical Extended Object Tracking
K. Thormann and M. Baum
2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2021.
BibTex
@inproceedings{2021_Thormann,
author = {Thormann, Kolja and Baum, Marcus},
booktitle = {2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
title = {Incorporating Range-Rate Measurements in {EKF}-based Elliptical Extended Object Tracking},
year = {2021},
month = sep
}
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Real-World Marine Radar Datasets for Evaluating Target Tracking Methods
J. S. Fowdur, M. Baum, and F. Heymann
Sensors, vol. 21, no. 14, Jul. 2021.
Abstract
BibTex
DOI
As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article.
@article{2021_Fowdur,
author = {Fowdur, Jaya Shradha and Baum, Marcus and Heymann, Frank},
journal = {Sensors},
title = {Real-World Marine Radar Datasets for Evaluating Target Tracking Methods},
year = {2021},
issn = {1424-8220},
month = jul,
number = {14},
volume = {21},
article-number = {4641},
doi = {10.3390/s21144641},
pubmedid = {34300381},
url = {https://www.mdpi.com/1424-8220/21/14/4641}
}
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Kalman Filter Based Extended Object Tracking with a Gaussian Mixture Spatial Distribution Model
K. Thormann, S. Yang, and M. Baum
2021 IEEE Intelligent Vehicles Symposium Workshops, 2021.
BibTex
@inproceedings{2021_Thormanna,
author = {Thormann, Kolja and Yang, Shishan and Baum, Marcus},
booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops},
title = {{K}alman Filter Based Extended Object Tracking with a {G}aussian Mixture Spatial Distribution Model},
year = {2021},
month = jul
}
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Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data
C. Malzer and M. Baum
Sensors, vol. 21, no. 10, May 2021.
BibTex
DOI
Source Code
@article{Sensors21_Malzer,
author = {Malzer, Claudia and Baum, Marcus},
journal = {Sensors},
title = {Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data},
year = {2021},
issn = {1424-8220},
month = may,
number = {10},
volume = {21},
article-number = {3410},
code = {https://github.com/Fusion-Goettingen/Constraint-based-Clustering},
doi = {10.3390/s21103410},
url = {https://www.mdpi.com/1424-8220/21/10/3410}
}
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Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths
K. Thormann and M. Baum
IEEE Transactions on Aerospace and Electronic Systems, 2021.
BibTex
PDF
DOI
Source Code
@article{2021_TAES_Thormann,
author = {Thormann, Kolja and Baum, Marcus},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
title = {Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths},
year = {2021},
code = {https://github.com/Fusion-Goettingen/EllipseFusion},
doi = {10.1109/TAES.2021.3057651},
file = {:https\://www.techrxiv.org/ndownloader/files/20098721:PDF},
url = {https://www.techrxiv.org/articles/Fusion_of_Elliptical_Extended_Object_Estimates_Parameterized_with_Orientation_and_Axes_Lengths/11336567/1}
}
2020
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Simulation-based Analysis of Multipath Delay Distributions in Urban Canyons
S. Ollander, F.-W. Bode, and M. Baum
2020 European Navigation Conference (ENC 2020), Dresden, Germany, 2020.
BibTex
Source Code
@inproceedings{2020_Ollandera,
author = {Ollander, Simon and Bode, Friedrich-Wilhelm and Baum, Marcus},
booktitle = {2020 European Navigation Conference (ENC 2020)},
title = {Simulation-based Analysis of Multipath Delay Distributions in Urban Canyons},
year = {2020},
address = {Dresden, Germany},
month = nov,
code = {https://github.com/Fusion-Goettingen/Bosch-GNSS-Reflection-Simulator}
}
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Assymetric Noise Tailoring for Vehicle Lidar data in Extended Object Tracking
H. Kaulbersch, J. Honer, and M. Baum
Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, 2020.
BibTex
DOI
@inproceedings{2020_Kaulbersch,
author = {Kaulbersch, Hauke and Honer, Jens and Baum, Marcus},
booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
title = {Assymetric Noise Tailoring for Vehicle Lidar data in Extended Object Tracking},
year = {2020},
address = {Virtual},
month = sep,
doi = {10.1109/MFI49285.2020.9235253}
}
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Deterministic Gibbs Sampling for Data Association in Multi-Object Tracking
L. Wolf and M. Baum
Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, 2020.
BibTex
DOI
Source Code
@inproceedings{2020_Wolf,
author = {Wolf, Laura and Baum, Marcus},
booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
title = {Deterministic Gibbs Sampling for Data Association in Multi-Object Tracking},
year = {2020},
address = {Virtual},
month = sep,
code = {https://github.com/Fusion-Goettingen/HerdedGibbs},
doi = {10.1109/MFI49285.2020.9235211},
url = {https://www.techrxiv.org/articles/Deterministic_Gibbs_Sampling_for_Data_Association_in_Multi-Object_Tracking/12435398/1}
}
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A Hybrid Approach To Hierarchical Density-based Cluster Selection
C. Malzer and M. Baum
Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, 2020.
BibTex
PDF
DOI
Source Code
@inproceedings{2020_Malzer,
author = {Malzer, Claudia and Baum, Marcus},
booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
title = {A Hybrid Approach To Hierarchical Density-based Cluster Selection},
year = {2020},
address = {Virtual},
month = sep,
code = {https://hdbscan.readthedocs.io/en/latest/how_to_use_epsilon.html},
doi = {10.1109/MFI49285.2020.9235263},
file = {1911.02282:https\://arxiv.org/pdf/1911.02282:PDF}
}
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A Comparison of Kalman Filter-based Approaches for Elliptic Extended Object Tracking
K. Thormann, S. Yang, and M. Baum
Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020), Virtual, 2020.
BibTex
DOI
Source Code
@inproceedings{2020_Fusion_Thormann,
author = {Thormann, Kolja and Yang, Shishan and Baum, Marcus},
booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
title = {A Comparison of Kalman Filter-based Approaches for Elliptic Extended Object Tracking},
year = {2020},
address = {Virtual},
month = jul,
code = {https://github.com/Fusion-Goettingen/KalmanEllipses},
doi = {10.23919/FUSION45008.2020.9190375}
}
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Marginal Association Probabilities for Multiple Extended Objects without Enumeration of Measurement Partitions
S. Yang, L. M. Wolf, and M. Baum
Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020), Virtual, 2020.
BibTex
DOI
@inproceedings{2020_Fusion_Yang,
author = {Yang, Shishan and Wolf, Laura M. and Baum, Marcus},
booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
title = {{Marginal Association Probabilities for Multiple Extended Objects without Enumeration of Measurement Partitions}},
year = {2020},
address = {Virtual},
month = jul,
doi = {10.23919/FUSION45008.2020.9190500}
}
-
Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons
S. Ollander, F. A. Schiegg, F.-W. Bode, and M. Baum
Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020), Virtual, 2020.
BibTex
DOI
@inproceedings{2020_Fusion_Ollander,
author = {Ollander, Simon and Schiegg, Florian Alexander and Bode, Friedrich-Wilhelm and Baum, Marcus},
booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
title = {Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons},
year = {2020},
address = {Virtual},
month = jul,
doi = {10.23919/FUSION45008.2020.9190612}
}
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Multipath Delay Estimation Using Signal Power Measurements from Multiple Carrier Frequencies
S. Ollander, F.-W. Bode, and M. Baum
2020 International Conference on Localization and GNSS (ICL-GNSS 2020), Tampere, Finland, 2020.
BibTex
DOI
@inproceedings{2020_Ollander,
author = {Ollander, Simon and Bode, Friedrich-Wilhelm and Baum, Marcus},
booktitle = {2020 International Conference on Localization and GNSS (ICL-GNSS 2020)},
title = {Multipath Delay Estimation Using Signal Power Measurements from Multiple Carrier Frequencies},
year = {2020},
address = {Tampere, Finland},
month = jun,
doi = {10.23919/FUSION45008.2020.9190612}
}
-
Simulation Science: Second International Workshop, SimScience 2019, Clausthal-Zellerfeld, May 8-10, 2019, Revised Selected Papers
N. Gunkelmann and M. Baum, Eds.
Communications in Computer and Information Science (CCIS), Springer International Publishing 2020.
BibTex
URL
@proceedings{2020_SimScience,
title = {Simulation Science: Second International Workshop, SimScience 2019, Clausthal-Zellerfeld, May 8-10, 2019, Revised Selected Papers},
year = {2020},
editor = {Gunkelmann, Nina and Baum, Marcus},
isbn = {978-3-030-45717-4},
month = jun,
number = {1},
publisher = {Communications in Computer and Information Science (CCIS), Springer International Publishing},
volume = {1199},
url = {https://www.springer.com/de/book/9783030457174}
}
-
On ABC Particle Filter Methods for Multiple Object Tracking
F. Sigges and M. Baum
Simulation Science: Second International Workshop, SimScience 2019, Clausthal-Zellerfeld, May 8-10, 2019, Revised Selected Papers, 2020.
BibTex
@inproceedings{2020_SWZ_Sigges,
author = {Sigges, Fabian and Baum, Marcus},
booktitle = {Simulation Science: Second International Workshop, SimScience 2019, Clausthal-Zellerfeld, May 8-10, 2019, Revised Selected Papers},
title = {{On ABC Particle Filter Methods for Multiple Object Tracking}},
year = {2020},
publisher = {Springer},
series = {Communications in Computer and Information Science (CCIS)}
}
2019
-
Improving Bimanual Interaction with a Prosthesis using Semi-Autonomous Control
R. Volkmar, S. Dosen, J. Gonzalez-Vargas, M. Baum, and M. Markovic
Journal of NeuroEngineering and Rehabilitation, vol. 16, no. 1, Nov. 2019.
Abstract
BibTex
DOI
The loss of a hand is a traumatic experience that substantially compromises an individualâs capability to interact with his environment. The myoelectric prostheses are state-of-the-art (SoA) functional replacements for the lost limbs. Their overall mechanical design and dexterity have improved over the last few decades, but the users have not been able to fully exploit these advances because of the lack of effective and intuitive control. Bimanual tasks are particularly challenging for an amputee since prosthesis control needs to be coordinated with the movement of the sound limb. So far, the bimanual activities have been often neglected by the prosthetic research community.
@article{2019_JNER_Volkmar,
author = {Volkmar, Robin and Dosen, Strahinja and Gonzalez-Vargas, Jose and Baum, Marcus and Markovic, Marko},
title = {{Improving Bimanual Interaction with a Prosthesis using Semi-Autonomous Control}},
journal = {Journal of NeuroEngineering and Rehabilitation},
year = {2019},
volume = {16},
number = {1},
pages = {140},
month = nov,
issn = {1743-0003},
doi = {10.1186/s12984-019-0617-6}
}
-
A Marine Radar Dataset for Multiple Extended Target Tracking
J. S. Fowdur, M. Baum, and F. Heymann
1st Maritime Situational Awareness Workshop (MSAW 2019), Villa Marigola, Lerici (La Spezia), Italy, 2019.
BibTex
@inproceedings{2019_MSAW_Fowdur,
author = {Fowdur, Jaya Shradha and Baum, Marcus and Heymann, Frank},
title = {{A Marine Radar Dataset for Multiple Extended Target Tracking}},
booktitle = {1st Maritime Situational Awareness Workshop (MSAW 2019)},
year = {2019},
address = {Villa Marigola, Lerici (La Spezia), Italy},
month = oct
}
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Tracking the Orientation and Axes Lengths of an Elliptical Extended Object
S. Yang and M. Baum
IEEE Transactions on Signal Processing, vol. 67, no. 18, Sep. 2019.
BibTex
PDF
DOI
Source Code
@article{2019_TSP_Yang,
author = {Yang, Shishan and Baum, Marcus},
title = {Tracking the Orientation and Axes Lengths of an Elliptical Extended Object},
journal = {IEEE Transactions on Signal Processing},
year = {2019},
volume = {67},
number = {18},
pages = {4720-4729},
month = sep,
issn = {1053-587X},
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/MEM-EKFstar},
doi = {10.1109/TSP.2019.2929462},
file = {:https\://arxiv.org/pdf/1805.03276:PDF},
keywords = {Shape;Noise measurement;Kinematics;Shape measurement;Object tracking;Kalman filters;Covariance matrices;Target tracking;extended object tracking;multiplicative error;Kalman filter},
url = {https://arxiv.org/abs/1805.03276}
}
-
The Dual-frequency Post-correlation Difference Feature for Detection of Multipath and non-Line-of-Sight Errors in Satellite Navigation
S. Ollander, F.-W. Bode, and M. Baum
22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, 2019.
Abstract
BibTex
Global Navigation Satellite Systems provide users with positioning in
outdoor environments, however their performance in urban areas is decreased
through errors caused by the reception of signals that are reflected on
buildings (multipath and non-line-of-sight errors). To detect these errors,
we simulated their influence on the correlator output on two carrier
frequencies. Based upon the simulations, a feature using the difference
between an ideal correlator output and the received correlator output was
developed. This article presents the new Dual-frequency Post-correlation
Difference feature and compares its performance with two methods from the
bibliography. This new feature outperforms the Code-Minus-Carrier and the
signal-to-noise ratio difference features in multipath and
non-line-of-sight detection. Furthermore it can distinguish between
multipath and non-line-of-sight reception. As a consequence, the method can
be used to exclude satellites whose signals are affected by reflections, to
provide a more accurate navigation solution.
@inproceedings{2019_Fusion_Ollander,
author = {Ollander, Simon and Bode, Friedrich-Wilhelm and Baum, Marcus},
title = {The Dual-frequency Post-correlation Difference Feature for Detection of Multipath and {non-Line-of-Sight} Errors in Satellite Navigation},
booktitle = {22nd International Conference on Information Fusion (FUSION 2019)},
year = {2019},
address = {Ottawa, Canada},
month = jul,
days = {2}
}
-
Optimal Fusion of Elliptic Extended Target Estimates Based on the Wasserstein Distance
K. Thormann and M. Baum
22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, 2019.
Abstract
BibTex
URL
Source Code
This paper considers the fusion of multiple estimates of a spatially extended object, where the object extent is modeled as an ellipse parameterized by the orientation and semi-axes lengths. For this purpose, we propose a novel systematic approach that employs a distance measure for ellipses, i.e., the Gaussian Wasserstein distance, as a cost function. We derive an explicit approximate expression for the Minimum Mean Gaussian Wasserstein distance (MMGW) estimate. Based on the concept of a MMGW estimator, we develop efficient methods for the fusion of extended target estimates. The proposed fusion methods are evaluated in a simulated experiment and the benefits of the novel methods are discussed.
@inproceedings{2019_Fusion_Thormann,
author = {Thormann, Kolja and Baum, Marcus},
title = {Optimal Fusion of Elliptic Extended Target Estimates Based on the Wasserstein Distance},
booktitle = {22nd International Conference on Information Fusion (FUSION 2019)},
year = {2019},
address = {Ottawa, Canada},
month = jul,
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/EllipseFusion},
days = {2},
url = {https://arxiv.org/abs/1904.00708}
}
-
Tracking Targets with Known Spatial Extent Using Experimental Marine Radar Data
J. S. Fowdur, M. Baum, and F. Heymann
22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, 2019.
Abstract
BibTex
Endeavours to have a reliable and robust maritime traffic situation
assessment are today leading to exploit the improved sensor resolution
technology of radars. In such concerned tracking scenarios, multiple noisy
scattered measurements arise
from targets of interest’s surfaces at each observation step. In this
paper, we present a special version of the Multiplicative Error
Model-Extended Kalman Filter* (MEM-EKF*) approach to recursively track the
orientation and kinematics of targets having known physical dimensions from
a maritime- and radar-based dataset. Altogether, we also introduce and
thereby propose an open dataset part of a marine radar benchmark dedicated
to extended target tracking to the community.
The benefits and performance of the proposed approach adopted on our
dataset are discussed taking into consideration the non-ideal but
nonetheless prevailing effects of the quotidian marine radar such as beam
spreading and false negatives. As compared to its original version, the
proposed approach proved to be more computationally efficient.
@inproceedings{2019_Fusion_Fowdur,
author = {Fowdur, Jaya Shradha and Baum, Marcus and Heymann, Frank},
title = {{Tracking Targets with Known Spatial Extent Using Experimental Marine Radar Data}},
booktitle = {22nd International Conference on Information Fusion (FUSION 2019)},
year = {2019},
address = {Ottawa, Canada},
month = jul
}
-
EM-based Extended Target Tracking with Automotive Radar Using Learned Spatial Distribution Models
H. Kaulbersch, J. Honer, and M. Baum
22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, 2019.
Abstract
BibTex
In this paper, a novel interpretation of associations to a learned model is
presented, which allows for the combination of algorithms for extended
target tracking in the context of the automotive sector. Specifically,
learning the spatial distribution of measurements from a vehicle in the
form of a Variational Gaussian Mixture (VGM) model. Which yields an
interpretation that is applicable for the Expectation Maximization (EM)
algorithm, such that a closed-form measurement update for tracking an
extended target can be derived. The approach is in particular designed for
sparse and noisy measurements and is applied to Radio Detection and Ranging
(RADAR) point information. Furthermore, an evaluation based on data from
the recent NuScenes data set is performed.
@inproceedings{2019_IV_Kaulbersch,
author = {Kaulbersch, Hauke and Honer, Jens and Baum, Marcus},
title = {{EM-based} Extended Target Tracking with Automotive Radar Using Learned Spatial Distribution Models},
booktitle = {22nd International Conference on Information Fusion (FUSION 2019)},
year = {2019},
address = {Ottawa, Canada},
month = jul,
days = {2}
}
-
Network Flow Labeling for Extended Target Tracking PHD filters
S. Yang, F. Teich, and M. Baum
IEEE Transactions on Industrial Informatics, 2019.
BibTex
DOI
@article{2019_TII_Yang,
author = {Yang, Shishan and Teich, Florian and Baum, Marcus},
journal = {IEEE Transactions on Industrial Informatics},
title = {Network Flow Labeling for Extended Target Tracking PHD filters},
year = {2019},
issn = {1551-3203},
pages = {1-1},
doi = {10.1109/TII.2019.2898992},
keywords = {Target tracking;Labeling;Shape;Time measurement;Radar tracking;Object tracking;Trajectory}
}
2018
-
Linear Programming based Time Lag Identification in Event Sequences
M. F. Huber, M.-A. Zöller, and M. Baum
Automatica, vol. 54, no. 12, Dec. 2018.
BibTex
DOI
Source Code
@article{2018_Huber,
author = {Huber, Marco F. and Zöller, Marc-André and Baum, Marcus},
journal = {Automatica},
title = {{Linear Programming based Time Lag Identification in Event Sequences}},
year = {2018},
month = dec,
number = {12},
volume = {54},
code = {https://github.com/Fusion-Goettingen/EventCorrelation},
doi = {10.1016/j.automatica.2018.08.025}
}
-
Simulation Science: First International Workshop, SimScience 2017, Göttingen, Germany, April 27-28, 2017, Revised Selected Papers
M. Baum, G. Brenner, J. Grabowski, T. Hanschke, S. Hartmann, and A. Schöbel, Eds.
Communications in Computer and Information Science (CCIS), Springer International Publishing 2018.
BibTex
URL
@proceedings{SimScience2018,
title = {Simulation Science: First International Workshop, SimScience 2017, Göttingen, Germany, April 27-28, 2017, Revised Selected Papers},
year = {2018},
editor = {Baum, Marcus and Brenner, Gunther and Grabowski, Jens and Hanschke, Thomas and Hartmann, Stefan and Schöbel, Anita},
volume = {889},
number = {1},
publisher = {Communications in Computer and Information Science (CCIS), Springer International Publishing},
month = oct,
isbn = {978-3-319-96270-2},
url = {https://www.springer.com/us/book/9783319962702}
}
-
Multi-Frequency GNSS Signal Fusion for Minimization of Multipath and Non-Line-of-Sight Errors: A Survey
S. Ollander, F.-W. Bode, and M. Baum
IEEE 15th Workshop on Positioning, Navigation and Communications (WPNC’18), 2018.
BibTex
@inproceedings{Ollander2018,
author = {Ollander, Simon and Bode, Friedrich-Wilhelm and Baum, Marcus},
title = {{Multi-Frequency GNSS Signal Fusion for Minimization of Multipath and Non-Line-of-Sight Errors: A Survey}},
booktitle = {IEEE 15th Workshop on Positioning, Navigation and Communications (WPNC'18)},
year = {2018},
month = oct
}
-
Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking
S. Yang, K. Thormann, and M. Baum
2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018), Sheffield, United Kingdom, 2018.
BibTex
DOI
Source Code
@inproceedings{Yang2018_SAM,
author = {Yang, Shishan and Thormann, Kolja and Baum, Marcus},
booktitle = {2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018)},
title = {{Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking}},
year = {2018},
address = {Sheffield, United Kingdom},
month = jul,
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/MEOT/linearJPDA},
days = {7},
doi = {10.1109/sam.2018.8448430}
}
-
Extended Target Tracking Using Gaussian Processes with High-Resolution Automotive Radar
K. Thormann, J. Honer, and M. Baum
21st International Conference on Information Fusion (FUSION 2018), Cambridge, United Kingdom, 2018.
BibTex
@inproceedings{Thormann2018_Fusion,
author = {Thormann, Kolja and Honer, Jens and Baum, Marcus},
title = {{Extended Target Tracking Using Gaussian Processes with {High-Resolution} Automotive Radar}},
booktitle = {21st International Conference on Information Fusion (FUSION 2018)},
year = {2018},
address = {Cambridge, United Kingdom},
month = jul
}
-
A Cartesian B-Spline Vehicle Model for Extended Object Tracking
H. Kaulbersch, J. Honer, and M. Baum
21st International Conference on Information Fusion (FUSION 2018), Cambridge, United Kingdom, 2018.
BibTex
Source Code
@inproceedings{Kaulbersch2018_Fusion,
author = {Kaulbersch, Hauke and Honer, Jens and Baum, Marcus},
title = {{A Cartesian {B-Spline} Vehicle Model for Extended Object Tracking}},
booktitle = {21st International Conference on Information Fusion (FUSION 2018)},
year = {2018},
address = {Cambridge, United Kingdom},
month = jul,
code = {https://github.com/Fusion-Goettingen/ExtendedTargetTrackingToolbox/blob/master/models/spline.py},
days = {9}
}
-
Post-Processing of Multi-Target Trajectories for Traffic Safety Analysis
T. Janz, A. Leich, M. Junghans, K. Gimm, S. Yang, and M. Baum
21st International Conference on Information Fusion (FUSION 2018), Cambridge, United Kingdom, 2018.
BibTex
@inproceedings{Janz2018_Fusion,
author = {Janz, Thorben and Leich, Andreas and Junghans, Marek and Gimm, Kay and Yang, Shishan and Baum, Marcus},
title = {{{Post-Processing} of {Multi-Target} Trajectories for Traffic Safety Analysis}},
booktitle = {21st International Conference on Information Fusion (FUSION 2018)},
year = {2018},
address = {Cambridge, United Kingdom},
month = jul
}
-
An Ensemble Kalman Filter for Feature-Based SLAM with Unknown Associations
F. Sigges, C. Rauterberg, M. Baum, and U. D. Hanebeck
21st International Conference on Information Fusion (FUSION 2018), Cambridge, United Kingdom, 2018.
BibTex
@inproceedings{Sigges2018_Fusion,
author = {Sigges, Fabian and Rauterberg, Christoph and Baum, Marcus and Hanebeck, Uwe D.},
title = {{An Ensemble Kalman Filter for {Feature-Based} {SLAM} with Unknown Associations}},
booktitle = {21st International Conference on Information Fusion (FUSION 2018)},
year = {2018},
address = {Cambridge, United Kingdom},
month = jul
}
-
A General Reliability-Aware Fusion Concept Using DST and Supervised Learning with Its Applications in Multi-Source Road Estimation
T. T. Nguyen, J. Spehr, D. Vock, M. Baum, S. Zug, and R. Kruse
Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2018), Changshu, Suzhou, China, 2018.
BibTex
@inproceedings{Nguyen_IV2018,
author = {Nguyen, T. T. and Spehr, J. and Vock, D. and Baum, M. and Zug, S. and Kruse, R.},
title = {{A General Reliability-Aware Fusion Concept Using DST and Supervised Learning with Its Applications in Multi-Source Road Estimation}},
booktitle = {Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2018)},
year = {2018},
address = {Changshu, Suzhou, China},
month = jun
}
-
Guest Editorial Special Section on Multisensor Fusion and Integration for Intelligent Systems
U. D. Hanebeck, M. Baum, and M. F. Huber, Eds.
IEEE Transactions on Industrial Informatics 2018.
BibTex
DOI
@proceedings{TII_Editorial_MFI2018,
title = {{Guest Editorial Special Section on Multisensor Fusion and Integration for Intelligent Systems}},
year = {2018},
editor = {Hanebeck, Uwe D. and Baum, Marcus and Huber, Marco F.},
volume = {14},
number = {3},
publisher = {IEEE Transactions on Industrial Informatics},
month = mar,
doi = {10.1109/TII.2018.2797956},
issn = {1551-3203}
}
-
Ensemble Kalman Filter Variants for Multi-Object Tracking with False and Missing Measurements, in Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Lecture Notes in Electrical Engineering (LNEE), Springer, 2018.
BibTex
URL
@incollection{Sigges2018_LNEE,
author = {Sigges, Fabian and Baum, Marcus},
title = {{Ensemble Kalman Filter Variants for Multi-Object Tracking with False and Missing Measurements}},
booktitle = {Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Lecture Notes in Electrical Engineering (LNEE)},
publisher = {Springer},
year = {2018},
comment = {Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
url = {https://link.springer.com/chapter/10.1007/978-3-319-90509-9_14}
}
-
Improving Ego-Lane Detection by Incorporating Source Reliability, in Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Lecture Notes in Electrical Engineering (LNEE), Springer, 2018.
BibTex
URL
@incollection{Nguyen2018_LNEE,
author = {Nguyen, T. T. and Spehr, J. and Sitzmann, J. and Baum, M. and Zug, S. and Kruse, R.},
title = {{Improving Ego-Lane Detection by Incorporating Source Reliability}},
booktitle = {Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Lecture Notes in Electrical Engineering (LNEE)},
publisher = {Springer},
year = {2018},
comment = {Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
url = {https://link.springer.com/chapter/10.1007/978-3-319-90509-9_6}
}
2017
-
Extended Object Tracking with Exploitation of Range Rate Measurements
S. Bordonaro, P. Willett, Y. B. Shalom, T. Luginbuhl, and M. Baum
ISIF Journal of Advances in Information Fusion, vol. 12, no. 2, Dec. 2017.
BibTex
PDF
@article{Bordonaro2017,
author = {Bordonaro, Steven and Willett, Peter and Shalom, Yaakov Bar and Luginbuhl, Tod and Baum, Marcus},
title = {{Extended Object Tracking with Exploitation of Range Rate Measurements}},
journal = {ISIF Journal of Advances in Information Fusion},
year = {2017},
volume = {12},
number = {2},
month = dec,
file = {:http\://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol12_2_Extended%20Object%20Tracking2.pdf:PDF}
}
-
Extended Object Tracking: Introduction, Overview and Applications
K. Granström, M. Baum, and S. Reuter
ISIF Journal of Advances in Information Fusion, vol. 12, no. 2, Dec. 2017.
Tutorial webpage
BibTex
PDF
@article{2017_JAIF_Granstroem,
author = {Granstr\"om, Karl and Baum, Marcus and Reuter, Stephan},
journal = {ISIF Journal of Advances in Information Fusion},
title = {{Extended Object Tracking: Introduction, Overview and Applications}},
year = {2017},
issn = {1557-6418},
month = dec,
number = {2},
volume = {12},
file = {JAIF_Vol12_2_Extended%20Object%20Tracking.pdf:http\://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol12_2_Extended%20Object%20Tracking.pdf:PDF},
webnote = { Tutorial webpage }
}
-
Guest Editorial: Special Issue on Extended Object Tracking
K. Granström and M. Baum, Eds.
ISIF Journal of Advances in Information Fusion 2017.
BibTex
PDF
URL
@proceedings{Granstroem2017b,
title = {{Guest Editorial: Special Issue on Extended Object Tracking}},
year = {2017},
editor = {Granstr\"om, Karl and Baum, Marcus},
volume = {12},
number = {2},
publisher = {ISIF Journal of Advances in Information Fusion},
month = dec,
file = {:http\://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol12_2_From%20the%20Editor-in-Chief.pdf:PDF},
keywords = {Editorial},
url = {http://isif.org/journal/12/2/1557-6418}
}
-
A Survey of Performance Measures to Evaluate Ego-Lane Estimation and A Novel Sensor-Independent Measure Along with Its Applications
T. T. Nguyen, J. Spehr, J. Xiong, M. Baum, S. Zug, and R. Kruse
Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, Korea, 2017.
Best Poster Paper Award
BibTex
DOI
@inproceedings{Nguyen2017,
author = {Nguyen, Tran Tuan and Spehr, Jens and Xiong, Jian and Baum, Marcus and Zug, Sebastian and Kruse, Rudolf},
title = {{A Survey of Performance Measures to Evaluate Ego-Lane Estimation and A Novel Sensor-Independent Measure Along with Its Applications}},
booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
year = {2017},
address = {Daegu, Korea},
month = nov,
note = {\textbf{ \color{red}Best Poster Paper Award}},
award = {Best Poster Paper Award},
doi = {10.1109/MFI.2017.8170435}
}
-
Online Reliability Assessment and Reliability-Aware Fusion for Ego-Lane Detection Using Influence Diagram and Bayes Filter
T. T. Nguyen, J. Spehr, J. Xiong, M. Baum, S. Zug, and R. Kruse
Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, Korea, 2017.
BibTex
DOI
@inproceedings{Nguyen2017a,
author = {Nguyen, Tran Tuan and Spehr, Jens and Xiong, Jian and Baum, Marcus and Zug, Sebastian and Kruse, Rudolf},
title = {{Online Reliability Assessment and Reliability-Aware Fusion for Ego-Lane Detection Using Influence Diagram and Bayes Filter}},
booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
year = {2017},
address = {Daegu, Korea},
month = nov,
doi = {10.1109/MFI.2017.8170400}
}
-
A Nearest Neighbour Ensemble Kalman Filter for Multi-Object Tracking
F. Sigges and M. Baum
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Daegu, South Korea, pp. pp. 227–232, 2017.
BibTex
DOI
@inproceedings{Sigges2017a,
title = {{A Nearest Neighbour Ensemble Kalman Filter for Multi-Object Tracking}},
author = {Sigges, Fabian and Baum, Marcus},
booktitle = {2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
year = {2017},
address = {Daegu, South Korea},
month = nov,
pages = {227--232},
doi = {10.1109/MFI.2017.8170433}
}
-
Fast Road Boundary Detection and Tracking in Occupancy Grids from Laser Scans
K. Thormann, J. Honer, and M. Baum
Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, Korea, 2017.
BibTex
DOI
@inproceedings{Thormann2017a,
author = {Thormann, Kolja and Honer, Jens and Baum, Marcus},
title = {{Fast Road Boundary Detection and Tracking in Occupancy Grids from Laser Scans}},
booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
year = {2017},
address = {Daegu, Korea},
month = nov,
doi = {10.1109/MFI.2017.8170453}
}
-
Random Finite Set Particle Filter for Source Enumeration and Direction-of-Arrival Tracking Using Sonar Arrays
B. Balasingam, M. Baum, and P. Willett
Proceedings of the 20th International Conference on Information Fusion (FUSION 2017), Xi’an, P.R. China, 2017.
BibTex
DOI
@inproceedings{Balasingam2017,
author = {Balasingam, Balakumar and Baum, Marcus and Willett, Peter},
title = {{Random Finite Set Particle Filter for Source Enumeration and Direction-of-Arrival Tracking Using Sonar Arrays}},
booktitle = {Proceedings of the 20th International Conference on Information Fusion (FUSION 2017)},
year = {2017},
address = {Xi'an, P.R. China},
month = jul,
days = {9},
doi = {10.23919/ICIF.2017.8009709}
}
-
EM Approach for Tracking Star-Convex Extended Objects
H. Kaulbersch, M. Baum, and P. Willett
Proceedings of the 20th International Conference on Information Fusion (Fusion 2017), Xi’an, P.R. China, 2017.
Abstract
BibTex
DOI
We develop an Expectation-Maximization (EM) algorithm for the simultaneous
tracking and shape estimation of a star-convex object based on multiple
spatially distributed measurements. In order to formulate the problem
within the EM framework, the unknown measurement sources on the object are
modeled as hidden variables. As the measurement sources are continuous
quantities, we develop a suitable discretization method that allows for a
closed-form EM iteration. The performance of the EM approach is
demonstrated in comparison with a recursive Gaussian filter based on the
Random Hypersurface Model (RHM).
@inproceedings{Kaulbersch2017,
author = {Kaulbersch, Hauke and Baum, Marcus and Willett, Peter},
title = {{EM Approach for Tracking Star-Convex Extended Objects}},
booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
year = {2017},
address = {Xi'an, P.R. China},
month = jul,
days = {9},
doi = {10.23919/ICIF.2017.8009884}
}
-
A Likelihood-Free Particle Filter for Multi-Object Tracking
F. Sigges, M. Baum, and U. D. Hanebeck
Proceedings of the 20th International Conference on Information Fusion (FUSION 2017), Xi’an, P.R. China, 2017.
Abstract
BibTex
DOI
We present a particle filter for multi-object tracking, which is based on
the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main
idea is to avoid the explicit computation of the likelihood function by
means of simulation. For this purpose, a large amount of candidate
particles is simulated, transformed into measurement space, and then
compared to the real measurement by using an appropriate distance function,
i.e., the OSPA distance. By selecting the closest simulated measurements
and their corresponding particles in state space, the posterior
distribution is approximated. The algorithm is evaluated in a multi-object
scenario with and without clutter and is compared to a global nearest
neighbour Kalman filter.
@inproceedings{Sigges2017,
author = {Sigges, Fabian and Baum, Marcus and Hanebeck, Uwe D},
title = {{A Likelihood-Free Particle Filter for Multi-Object Tracking}},
booktitle = {Proceedings of the 20th International Conference on Information Fusion (FUSION 2017)},
year = {2017},
address = {Xi'an, P.R. China},
month = jul,
days = {9},
doi = {10.23919/ICIF.2017.8009796}
}
-
Learning an Object Tracker with a Random Forest and Simulated Measurements
K. Thormann, F. Sigges, and M. Baum
Proceedings of the 20th International Conference on Information Fusion (FUSION 2017), Xi’an, P.R. China, 2017.
BibTex
DOI
@inproceedings{Thormann2017,
author = {Thormann, Kolja and Sigges, Fabian and Baum, Marcus},
title = {{Learning an Object Tracker with a Random Forest and Simulated Measurements}},
booktitle = {Proceedings of the 20th International Conference on Information Fusion (FUSION 2017)},
year = {2017},
address = {Xi'an, P.R. China},
month = jul,
days = {9},
doi = {10.23919/ICIF.2017.8009674}
}
-
Framework for Mining Event Correlations and Time Lags in Event Sequences
M.-A. Zöller, M. Baum, and M. Huber
IEEE 15th International Conference on Industrial Informatics (INDIN 2017), Emden, Germany, 2017.
BibTex
DOI
@inproceedings{Zoller2017,
author = {Z{\"o}ller, Marc-Andre and Baum, Marcus and Huber, Marco},
title = {{Framework for Mining Event Correlations and Time Lags in Event Sequences}},
booktitle = {IEEE 15th International Conference on Industrial Informatics (INDIN 2017)},
year = {2017},
address = {Emden, Germany},
month = jul,
doi = {10.1109/INDIN.2017.8104876},
owner = {baum},
timestamp = {2017.05.19}
}
-
Cognitive Video Streaming
D. Pasupuleti, P. Mannaru, B. Balasingam, M. Baum, K. Pattipati, P. Willett, C. Lintz, G. Commeau, F. Dorigo, and J. Fahrny
ISIF Journal of Advances in Information Fusion, vol. 12, no. 1, Jun. 2017.
BibTex
PDF
@article{J_Bala2017,
author = {Pasupuleti, Devaki and Mannaru, Pujitha and Balasingam, Balakumar and Baum, Marcus and Pattipati, Krishna and Willett, Peter and Lintz, Christopher and Commeau, Gabriel and Dorigo, Francesco and Fahrny, Jim},
title = {{Cognitive Video Streaming}},
journal = {ISIF Journal of Advances in Information Fusion},
year = {2017},
volume = {12},
number = {1},
month = jun,
file = {:https\://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol12_1_complete_170828_3.pdf:PDF}
}
-
GM-PHD filter for Multiple Extended Object Tracking based on the Multiplicative Error Shape Model and Network Flow Labeling
F. Teich, S. Yang, and M. Baum
Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2017), Redondo Beach, CA, USA, 2017.
BibTex
DOI
@inproceedings{Teich2017,
author = {Teich, Florian and Yang, Shishan and Baum, Marcus},
title = {{GM-PHD filter for Multiple Extended Object Tracking based on the Multiplicative Error Shape Model and Network Flow Labeling}},
booktitle = {Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2017)},
year = {2017},
address = {Redondo Beach, CA, USA},
month = jun,
doi = {10.1109/IVS.2017.7995691}
}
-
Symmetrizing Measurement Equations for Association-free Multi-target Tracking via Point Set Distances
U. D. Hanebeck, M. Baum, and P. Willett
SPIE - Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, Anaheim, California, USA, 2017.
BibTex
DOI
@inproceedings{Hanebeck2017,
author = {Hanebeck, Uwe D and Baum, Marcus and Willett, Peter},
title = {{Symmetrizing Measurement Equations for Association-free Multi-target Tracking via Point Set Distances}},
booktitle = {SPIE - Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI},
year = {2017},
address = {Anaheim, California, USA},
month = apr,
doi = {10.1117/12.2266988}
}
-
Comparative Evaluation for Recommender Systems for Book Recommendations
A. Tashkandi, L. Wiese, and M. Baum
Datenbanksysteme für Business, Technologie und Web (BTW 2017), 17. Fachtagung des GI Fachbereichs Datenbanken und Informationssysteme (DBIS), pp. pp. 291–300, 2017.
BibTex
PDF
@inproceedings{Tashkandi2017,
author = {Tashkandi, Araek and Wiese, Lena and Baum, Marcus},
title = {{Comparative Evaluation for Recommender Systems for Book Recommendations}},
booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2017), 17. Fachtagung des GI Fachbereichs Datenbanken und Informationssysteme (DBIS)},
year = {2017},
pages = {291--300},
month = mar,
file = {:http\://btw2017.informatik.uni-stuttgart.de/slidesandpapers/F-11-88/paper_web.pdf:PDF}
}
-
Extended Kalman Filter for Extended Object Tracking
S. Yang and M. Baum
Proceedings of the 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), New Orleans, USA, 2017.
BibTex
DOI
Source Code
@inproceedings{Yang2017_ICASSP,
author = {Yang, Shishan and Baum, Marcus},
title = {{Extended Kalman Filter for Extended Object Tracking}},
booktitle = {Proceedings of the 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017)},
year = {2017},
address = {New Orleans, USA},
month = mar,
code = {http://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/MEM_EKF},
doi = {10.1109/ICASSP.2017.7952985}
}
2016
-
Conveying System, Plant for Sorting Bulk Goods having a Conveying System of this Type, and Transport method. Google Patents, Dec-2016.
BibTex
URL
@misc{Gruna2016,
title = {Conveying System, Plant for Sorting Bulk Goods having a Conveying System of this Type, and Transport method},
author = {Gruna, R. and Vieth, K.U. and Schulte, H. and Langle, T. and Hanebeck, U. and Baum, Marcus and Noack, B.},
month = dec,
note = {US Patent App. 15/119,019},
year = {2016},
patent = {US Patent},
publisher = {Google Patents},
url = {https://www.google.com/patents/US20160354809}
}
-
Level-Set Random Hypersurface Models for Tracking Non-Convex Extended Objects
A. Zea, F. Faion, M. Baum, and U. Hanebeck
IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 6, Dec. 2016.
BibTex
DOI
@article{2016_TAES_Zea,
author = {Zea, Antonio and Faion, Florian and Baum, Marcus and Hanebeck, Uwe},
title = {{Level-Set Random Hypersurface Models for Tracking Non-Convex Extended Objects}},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
year = {2016},
volume = {52},
number = {6},
pages = {2990-3007},
month = dec,
issn = {0018-9251},
doi = {10.1109/TAES.2016.130704}
}
-
An EM Approach for Contour Tracking based on Point Clouds
H. Kaulbersch, M. Baum, and P. Willett
Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany, 2016.
BibTex
URL
@inproceedings{MFI16_Kaulbersch,
author = {Kaulbersch, Hauke and Baum, Marcus and Willett, Peter},
title = {{An EM Approach for Contour Tracking based on Point Clouds}},
booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
year = {2016},
address = {Baden-Baden, Germany},
month = sep,
url = {https://ieeexplore.ieee.org/document/7849542/}
}
-
Metrics for Performance Evaluation of Elliptic Extended Object Tracking Methods
S. Yang, M. Baum, and K. Granström
Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany, 2016.
BibTex
URL
Source Code
@inproceedings{2016_MFI_Yang,
author = {Yang, Shishan and Baum, Marcus and Granstr\"om, Karl},
title = {{Metrics for Performance Evaluation of Elliptic Extended Object Tracking Methods}},
booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
year = {2016},
address = {Baden-Baden, Germany},
month = sep,
code = {http://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/Evaluation},
url = {https://ieeexplore.ieee.org/document/7849541/}
}
-
The Kernel-SME Filter with False and Missing Measurements
M. Baum, S. Yang, and U. D. Hanebeck
19th International Conference on Information Fusion (Fusion 2016), Heidelberg, Germany, 2016.
Abstract
BibTex
PDF
URL
The recently proposed Kernel-SME filter for multi-object tracking is a further development of the Symmetric Measurement Equation (SME) idea introduced by Kamen in the 1990s. The Kernel-SME constructs a symmetric, i.e., permutation invariant, measurement equation by transforming the measurements to a kernel mixture function. This transformation is scalable to a large number of objects and allows for deriving an efficient closed-form Gaussian filter based on the Kalman filter formulas. This work shows how the Kernel-SME approach can systematically incorporate false and missing measurements.
@inproceedings{Baum2016,
author = {Baum, Marcus and Yang, Shishan and Hanebeck, Uwe D},
booktitle = {19th International Conference on Information Fusion (Fusion 2016)},
title = {{The Kernel-SME Filter with False and Missing Measurements}},
year = {2016},
address = {Heidelberg, Germany},
month = jul,
days = {4},
file = {:http\://isas.iar.kit.edu/pdf/Fusion16_Kernel-SME_Filter.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7527919/}
}
-
State Estimation Considering Negative Information with Switching Kalman and Ellipsoidal Filtering
B. Noack, F. Pfaff, M. Baum, and U. D. Hanebeck
19th International Conference on Information Fusion (Fusion 2016), Heidelberg, Germany, 2016.
Abstract
BibTex
PDF
URL
State estimation concepts like the Kalman filter heavily rely on potentially noisy sensor data. In general, the estimation quality depends on the amount of sensor data that can be exploited. However, missing observations do not necessarily impair the estimation quality but may also convey exploitable information on the system state. This type of information - noted as negative information - often requires specific measurement and noise models in order to take advantage of it. In this paper, a hybrid Kalman filter concept is employed that allows using both stochastic and set-membership representations of information. In particular, the latter representation is intended to account for negative information, which can often be easily described as a bounded set in the measurement space. Depending on the type of information, the filtering step of the proposed estimator adaptively switches between Gaussian and ellipsoidal noise representations. A target tracking scenario is studied to evaluate and discuss the proposed concept.
@inproceedings{Noack2016,
author = {Noack, Benjamin and Pfaff, Florian and Baum, Marcus and Hanebeck, Uwe D},
title = {{State Estimation Considering Negative Information with Switching Kalman and Ellipsoidal Filtering}},
booktitle = {19th International Conference on Information Fusion (Fusion 2016)},
year = {2016},
address = {Heidelberg, Germany},
month = jul,
days = {4},
file = {Fusion16_Noack.pdf:http\://isas.iar.kit.edu/pdf/Fusion16_Noack.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7528121/}
}
-
Second-Order Extended Kalman Filter for Extended Object and Group Tracking
S. Yang and M. Baum
Proceedings of the 19th International Conference on Information Fusion (Fusion 2016), Heidelberg, Germany, 2016.
Abstract
BibTex
URL
Source Code
In this paper, we propose a novel method for estimating an elliptic shape approximation of a moving extended object that gives rise to multiple scattered measurements per frame. For this purpose, we parameterize the elliptic shape with its orientation and lengths of the semi-axes. We relate an individual measurement with the ellipse parameters by means of a multiplicative noise model and derive a second-order extended Kalman filter for a closed-form recursive measurement update. The performance of the proposed method is illustrated by means of Monte Carlo simulations for both static and dynamic scenarios.
@inproceedings{Yang2016,
author = {Yang, Shishan and Baum, Marcus},
booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
title = {{Second-Order Extended Kalman Filter for Extended Object and Group Tracking}},
year = {2016},
address = {Heidelberg, Germany},
month = jul,
code = {http://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/MEM_SOEKF},
days = {4},
url = {http://ieeexplore.ieee.org/document/7528018/}
}
-
PMHT Approach for Underwater Bearing-Only Multisensor-Multitarget Tracking in Clutter
X. Li, P. Willett, M. Baum, and Y. Li
IEEE Journal of Oceanic Engineering, vol. PP, no. 99, 2016.
BibTex
DOI
@article{J_Li2016,
title = {{PMHT Approach for Underwater Bearing-Only Multisensor-Multitarget Tracking in Clutter}},
author = {Li, X. and Willett, P. and Baum, Marcus and Li, Y.},
journal = {IEEE Journal of Oceanic Engineering},
year = {2016},
number = {99},
pages = {1-9},
volume = {PP},
doi = {10.1109/JOE.2015.2506220},
issn = {0364-9059}
}
2015
-
On Wasserstein Barycenters and MMOSPA Estimation
M. Baum, P. Willett, and U. D. Hanebeck
IEEE Signal Processing Letters, vol. 22, no. 10, Oct. 2015.
BibTex
DOI
@article{2015_SPL_Baum,
author = {Baum, Marcus and Willett, Peter and Hanebeck, Uwe D.},
title = {{On Wasserstein Barycenters and MMOSPA Estimation}},
journal = {IEEE Signal Processing Letters},
year = {2015},
volume = {22},
number = {10},
pages = {1511-1515},
month = oct,
doi = {10.1109/LSP.2015.2410217}
}
-
Recursive Bayesian Pose and Shape Estimation of 3D Objects Using Transformed Plane Curves
F. Faion, A. Zea, J. Steinbring, M. Baum, and U. D. Hanebeck
Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015), Bonn, Germany, 2015.
BibTex
PDF
DOI
@inproceedings{Faion2015a,
author = {Faion, Florian and Zea, Antonio and Steinbring, Jannik and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Recursive Bayesian Pose and Shape Estimation of 3D Objects Using Transformed Plane Curves}},
booktitle = {Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)},
year = {2015},
address = {Bonn, Germany},
month = oct,
doi = {10.1109/SDF.2015.7347698},
file = {:http\://isas.iar.kit.edu/pdf/SDF15_Faion.pdf:PDF}
}
-
Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations
M. Baum, B. Noack, and U. D. Hanebeck
Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), San Diego, California, USA, 2015.
BibTex
PDF
DOI
@inproceedings{MFI15_Baum,
author = {Baum, Marcus and Noack, Benjamin and Hanebeck, Uwe D.},
title = {{Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations}},
booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015)},
year = {2015},
address = {San Diego, California, USA},
month = sep,
doi = {10.1109/MFI.2015.7295744},
file = {MFI15_Baum.pdf:http\://isas.iar.kit.edu/pdf/MFI15_Baum.pdf:PDF}
}
-
Depth Sensor Calibration by Means of Tracking an Extended Object
F. Faion, M. Baum, and U. D. Hanebeck
Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), San Diego, California, USA, 2015.
BibTex
PDF
@inproceedings{MFI15_Faion,
title = {{Depth Sensor Calibration by Means of Tracking an Extended Object}},
author = {Faion, Florian and Baum, Marcus and Hanebeck, Uwe D.},
booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015)},
year = {2015},
address = {San Diego, California, USA},
month = sep,
file = {MFI15_Faion.pdf:http\://isas.iar.kit.edu/pdf/MFI15_Faion.pdf:PDF}
}
-
State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-membership Pseudo Measurements
B. Noack, M. Baum, and U. D. Hanebeck
Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), San Diego, California, USA, 2015.
BibTex
PDF
DOI
@inproceedings{MFI15_Noack,
author = {Noack, Benjamin and Baum, Marcus and Hanebeck, Uwe D.},
title = {{State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-membership Pseudo Measurements}},
booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015)},
year = {2015},
address = {San Diego, California, USA},
month = sep,
doi = {10.1109/MFI.2015.7295824},
file = {MFI15_Noack.pdf:http\://isas.iar.kit.edu/pdf/MFI15_Noack.pdf:PDF}
}
-
TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials
F. Pfaff, M. Baum, B. Noack, U. D. Hanebeck, R. Gruna, T. Längle, and J. Beyerer
Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), San Diego, California, USA, 2015.
BibTex
PDF
DOI
@inproceedings{MFI15_Pfaff,
author = {Pfaff, Florian and Baum, Marcus and Noack, Benjamin and Hanebeck, Uwe D. and Gruna, Robin and Längle, Thomas and Beyerer, Jürgen},
title = {{TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials}},
booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015)},
year = {2015},
address = {San Diego, California, USA},
month = sep,
doi = {10.1109/MFI.2015.7295737},
file = {:http\://isas.iar.kit.edu/pdf/MFI15_Pfaff.pdf:PDF}
}
-
A Closed-Form Likelihood for Particle Filters to Track Extended Objects with Star-Convex RHMs
J. Steinbring, M. Baum, A. Zea, F. Faion, and U. D. Hanebeck
Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), San Diego, California, USA, 2015.
BibTex
PDF
DOI
@inproceedings{MFI15_Steinbring,
author = {Steinbring, Jannik and Baum, Marcus and Zea, Antonio and Faion, Florian and Hanebeck, Uwe D.},
title = {{A Closed-Form Likelihood for Particle Filters to Track Extended Objects with Star-Convex RHMs}},
booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015)},
year = {2015},
address = {San Diego, California, USA},
month = sep,
doi = {10.1109/MFI.2015.7295740},
file = {:http\://isas.iar.kit.edu/pdf/MFI15_Steinbring.pdf:PDF}
}
-
MMOSPA Estimation with Unknown Number of Objects
B. Balasingam, M. Baum, and P. Willett
IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Chengdu, China, 2015.
BibTex
DOI
@inproceedings{Balasingam2015,
author = {Balasingam, Balakumar and Baum, Marcus and Willett, Peter},
title = {{MMOSPA Estimation with Unknown Number of Objects}},
booktitle = {IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)},
year = {2015},
address = {Chengdu, China},
month = jul,
doi = {10.1109/ChinaSIP.2015.7230496}
}
-
OSPA Barycenters for Clustering Set-Valued Data
M. Baum, B. Balasingam, P. Willett, and U. D. Hanebeck
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
PDF
URL
@inproceedings{Baum2015,
author = {Baum, Marcus and Balasingam, Balakumar and Willett, Peter and Hanebeck, Uwe D},
title = {{OSPA Barycenters for Clustering Set-Valued Data}},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
file = {Fusion15_Baum.pdf:http\://isas.iar.kit.edu/pdf/Fusion15_Baum.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7266717/}
}
-
MMOSPA-based Direction-of-Arrival Tracking with a Passive Sonar Array - An Experimental Study
M. Baum and P. Willett
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
URL
@inproceedings{Baum2015a,
title = {{MMOSPA-based Direction-of-Arrival Tracking with a Passive Sonar Array - An Experimental Study}},
author = {Baum, Marcus and Willett, Peter},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
url = {http://ieeexplore.ieee.org/abstract/document/7266718/}
}
-
Partial Likelihood for Unbiased Extended Object Tracking
F. Faion, A. Zea, M. Baum, and U. D. Hanebeck
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
PDF
URL
@inproceedings{Faion2015,
author = {Faion, Florian and Zea, Antonio and Baum, Marcus and Hanebeck, Uwe D},
title = {{Partial Likelihood for Unbiased Extended Object Tracking}},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
file = {Fusion15_Faion.pdf:http\://isas.iar.kit.edu/pdf/Fusion15_Faion.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7266671/}
}
-
Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures
U. D. Hanebeck and M. Baum
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
PDF
URL
@inproceedings{Hanebeck2015,
author = {Hanebeck, Uwe D and Baum, Marcus},
title = {{Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures}},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
file = {Fusion15_Hanebeck-AssociationFreeTracking.pdf:http\://isas.iar.kit.edu/pdf/Fusion15_Hanebeck-AssociationFreeTracking.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7266716/}
}
-
Online Playtime Prediction for Cognitive Video Streaming
D. Pasupuleti, P. Mannaru, B. Balasingam, M. Baum, K. R. Pattipati, P. Willett, C. Lintz, G. Commeau, F. Dorigo, and J. Fahrny
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
PDF
URL
@inproceedings{Pasupuleti2015a,
author = {Pasupuleti, Devaki and Mannaru, Pujitha and Balasingam, Balakumar and Baum, Marcus and Pattipati, Krishna R and Willett, Peter and Lintz, Christopher and Commeau, Gabriel and Dorigo, Francesco and Fahrny, Jim},
title = {{Online Playtime Prediction for Cognitive Video Streaming}},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
file = {:http\://c4i.gmu.edu/~pcosta/F15/data/fileserver/file/472077/filename/Paper_1570113789.pdf:PDF},
url = {http://ieeexplore.ieee.org/document/7266785/}
}
-
The GFMT HPMHT Puzzle
P. Willett, T. E. Luginbuhl, and M. Baum
Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington, USA, 2015.
BibTex
PDF
URL
@inproceedings{Willett2015,
author = {Willett, Peter and Luginbuhl, Tod E and Baum, Marcus},
title = {{The GFMT HPMHT Puzzle}},
booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
year = {2015},
address = {Washington, USA},
month = jul,
file = {:http\://c4i.gmu.edu/~pcosta/F15/data/fileserver/file/472280/filename/Paper_1570109461.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/7266581/}
}
-
Symmetries in Bayesian Extended Object Tracking
F. Faion, A. Zea, M. Baum, and U. D. Hanebeck
Journal of Advances in Information Fusion, vol. 10, no. 1, Jun. 2015.
Abstract
BibTex
PDF
In this work, we exploit geometric symmetries in extended
objects in order to improve Bayesian tracking algorithms that use
Spatial Distribution Models, Greedy Association Models as used in
curve fitting, and Random Hypersurface Models. The key idea is to
describe symmetric objects by solely modeling the non-redundant
part of the shape, while the remainder of the shape follows from
symmetry. Following this idea, we develop simplified versions for
the three models that take advantage of the symmetry. Exploiting
symmetries yields two major benefits. First, complex symmetric
shapes can be equivalently represented by a fraction of the original
shape parameters. Second, when using sample-based filters, such as
the widely used Unscented Kalman Filter, symmetry yields a higher
effective sample resolution. It is worth mentioning that estimating
even simple objects such as a stick, which only have one reflectional
symmetry, can be significantly improved.
@article{J_Faion2014,
title = {{Symmetries in Bayesian Extended Object Tracking}},
author = {Faion, Florian and Zea, Antonio and Baum, Marcus and Hanebeck, Uwe D.},
journal = {Journal of Advances in Information Fusion},
year = {2015},
month = jun,
number = {1},
pages = {13-30},
volume = {10},
file = {433_1_art_9_24888.pdf:http\://confcats_isif.s3.amazonaws.com/web-files/journals/entries/433_1_art_9_24888.pdf:PDF}
}
-
Polynomial-Time Algorithms for the Exact MMOSPA Estimate of a Multi-Object Probability Density Represented by Particles
M. Baum, P. Willett, and U. D. Hanebeck
IEEE Transactions on Signal Processing, vol. 63, no. 10, May 2015.
BibTex
DOI
@article{2015_TSP_Baum,
author = {Baum, Marcus and Willett, Peter and Hanebeck, Uwe D.},
title = {{Polynomial-Time Algorithms for the Exact MMOSPA Estimate of a Multi-Object Probability Density Represented by Particles}},
journal = {IEEE Transactions on Signal Processing},
year = {2015},
volume = {63},
number = {10},
pages = {2476-2484},
month = may,
doi = {10.1109/TSP.2015.2403292}
}
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Extracting Speed, Heading and Turn-Rate Measurements from Extended Objects Using the EM Algorithm
S. Bordonaro, P. Willett, Y. B. Shalom, M. Baum, and T. Luginbuhl
IEEE Aerospace Conference, Big Sky, Montana, USA, 2015.
BibTex
DOI
@inproceedings{Bordonaro2015,
title = {{Extracting Speed, Heading and Turn-Rate Measurements from Extended Objects Using the EM Algorithm}},
author = {Bordonaro, Steven and Willett, Peter and Shalom, Yaakov Bar and Baum, Marcus and Luginbuhl, Tod},
booktitle = {IEEE Aerospace Conference},
year = {2015},
address = {Big Sky, Montana, USA},
month = mar,
doi = {10.1109/AERO.2015.7119195}
}
-
Cognitive Video Streaming
D. Pasupuleti, P. Mannaru, B. Balasingam, M. Baum, K. R. Pattipati, and W. P.
International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS), Vijayawada, Andhra Pradesh, India, 2015.
BibTex
@inproceedings{Pasupuleti2015,
title = {{Cognitive Video Streaming}},
author = {Pasupuleti, D. and Mannaru, P. and Balasingam, B. and Baum, Marcus and Pattipati, K. R. and P., Willett},
booktitle = {International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS)},
year = {2015},
address = {Vijayawada, Andhra Pradesh, India},
month = mar
}
-
Linear-time JPDAF based on Many-2-Many Approximation of Marginal Association Probabilities
M. Baum
Electronics Letters, vol. 51, 2015.
BibTex
DOI
@article{J_Baum2015,
title = {{Linear-time JPDAF based on Many-2-Many Approximation of Marginal Association Probabilities}},
author = {Baum, Marcus},
journal = {Electronics Letters},
year = {2015},
pages = {1526-1528},
volume = {51},
doi = {10.1049/el.2015.1411}
}
2014
-
MMOSPA-based Track Extraction in the PHD Filter – A Justification for k-Means Clustering
M. Baum, P. Willett, and U. D. Hanebeck
Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), Los Angeles, California, USA, 2014.
BibTex
PDF
DOI
@inproceedings{Baum2014b,
author = {Baum, Marcus and Willett, Peter and Hanebeck, Uwe D.},
title = {{MMOSPA-based Track Extraction in the PHD Filter -- A Justification for k-Means Clustering}},
booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
year = {2014},
address = {Los Angeles, California, USA},
month = dec,
doi = {10.1109/CDC.2014.7039662},
file = {:http\://isas.iar.kit.edu/pdf/CDC14_Baum.pdf:PDF}
}
-
Bayesian Estimation of Line Segments
F. Faion, A. Zea, M. Baum, and U. D. Hanebeck
Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014), Bonn, Germany, 2014.
BibTex
PDF
DOI
@inproceedings{Faion2014,
author = {Faion, Florian and Zea, Antonio and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Bayesian Estimation of Line Segments}},
booktitle = {Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)},
year = {2014},
address = {Bonn, Germany},
month = oct,
doi = {10.1109/SDF.2014.6954710},
file = {:isas.iar.kit.edu/pdf/SDF14_Faion.pdf:PDF}
}
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Evaluation of the PMHT Approach for Passive Radar Tracking with Unknown Transmitter Associations
X. Li, M. Baum, P. Willett, and Y. Li
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, 2014.
BibTex
PDF
URL
@inproceedings{Li2014,
author = {Li, Xiaohua and Baum, Marcus and Willett, Peter and Li, Ya'an},
title = {{Evaluation of the PMHT Approach for Passive Radar Tracking with Unknown Transmitter Associations}},
booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
year = {2014},
address = {Salamanca, Spain},
month = jul,
file = {:http\://confcats_isif.s3.amazonaws.com/web-files/event/proceedings/html/2014Proceedings/papers/fusion2014_submission_388/paper388.pdf:PDF},
url = {http://ieeexplore.ieee.org/document/6916268/}
}
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MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays
M. Baum, P. Willett, and U. D. Hanebeck
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, 2014.
Abstract
BibTex
PDF
DOI
This work is concerned with direction-of-arrival (DOA) estimation of narrowband signals from multiple targets using a planar antenna array. We illustrate the shortcomings of Maximum Likelihood (ML), Maximum a Posteriori (MAP), and Minimum Mean Squared Error (MMSE) estimation, issues that can be attributed to the symmetry in the likelihood function that must exist when there is no information about labeling of targets. We proffer the recently introduced concept of Minimum Mean OSPA (MMOSPA) estimation that is based on the optimal subpattern assignment (OSPA) metric for sets and hence inherently incorporates symmetric likelihood functions.
@inproceedings{SAM14_Baum,
author = {Baum, Marcus and Willett, Peter and Hanebeck, Uwe D.},
title = {{MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays}},
booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
year = {2014},
address = {A Coru{\~{n}}a, Spain},
month = jun,
doi = {10.1109/SAM.2014.6882377},
file = {SAM14_Baum.pdf:http\://isas.iar.kit.edu/pdf/SAM14_Baum.pdf:PDF}
}
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Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery
G. Kurz, M. Baum, and U. D. Hanebeck
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, 2014.
Abstract
BibTex
PDF
DOI
Performing surgery on the beating heart has significant advantages for the patient compared to traditional heart surgery on the stopped heart. A remote-controlled robot can be used to automatically cancel out the movement of the beating heart. This necessitates precise tracking of the heart surface. For this purpose, we track 24 identical artificial markers placed on the heart. This creates a data association problem, because it is not known which measurement was obtained from which marker. To solve this problem, we apply a multiple target tracking method based on a symmetric kernel transformation. This method allows efficient handling of the data association problem even for a reasonably large number of targets. We demonstrate how to implement this method efficiently. The proposed approach is evaluated on in-vivo data of a real beating heart surgery performed on a porcine beating heart.
@inproceedings{SAM14_KurzBaum,
author = {Kurz, Gerhard and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery}},
booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
year = {2014},
address = {A Coru{\~{n}}a, Spain},
month = jun,
doi = {10.1109/SAM.2014.6882375},
file = {SAM14_Kurz.pdf:http\://isas.iar.kit.edu/pdf/SAM14_Kurz.pdf:PDF}
}
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Tracking Simplified Shapes Using a Stochastic Boundary
A. Zea, F. Faion, M. Baum, and U. D. Hanebeck
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, 2014.
Abstract
BibTex
PDF
DOI
When tracking extended objects, it is often the case that the shape of the target cannot be fully observed due to issues of visibility, artifacts, or high noise, which can change with time. In these situations, it is a common approach to model targets as simpler shapes instead, such as ellipsoids or cylinders. However, these simplifications cause information loss from the original shape, which could be used to improve the estimation results. In this paper, we propose a way to recover information from these lost details in the form of a stochastic boundary, whose parameters can be dynamically estimated from received measurements. The benefits of this approach are evaluated by tracking an object using noisy, real-life RGBD data.
@inproceedings{SAM14_Zea,
author = {Zea, Antonio and Faion, Florian and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Tracking Simplified Shapes Using a Stochastic Boundary}},
booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
year = {2014},
address = {A Coru{\~{n}}a, Spain},
month = jun,
doi = {10.1109/SAM.2014.6882380},
file = {SAM14_Zea.pdf:http\://isas.iar.kit.edu/pdf/SAM14_Zea.pdf:PDF}
}
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Extended Object Tracking with Random Hypersurface Models
M. Baum and U. D. Hanebeck
IEEE Transactions on Aerospace and Electronic Systems, vol. 50, Jan. 2014.
BibTex
DOI
Source Code
@article{2014_TAES_Baum,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Extended Object Tracking with Random Hypersurface Models}},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
year = {2014},
volume = {50},
pages = {149-159},
month = jan,
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/RandomHypersurfaceModel},
doi = {10.1109/TAES.2013.120107}
}
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A Hybrid Data Association Model for Efficient Multi-Target Maximum Likelihood Estimation
M. Baum and P. Willett
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
BibTex
DOI
@inproceedings{Baum2014,
title = {{A Hybrid Data Association Model for Efficient Multi-Target Maximum Likelihood Estimation}},
author = {Baum, Marcus and Willett, Peter},
booktitle = {2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2014},
doi = {10.1109/ICASSP.2014.6854395}
}
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Approximate Calculation of Marginal Association Probabilities using a Hybrid Data Association Model
M. Baum, P. Willett, Y. Bar-Shalom, and U. D. Hanebeck
SPIE - Signal and Data Processing of Small Targets 2014, 2014.
BibTex
DOI
@inproceedings{Baum2014a,
title = {{Approximate Calculation of Marginal Association Probabilities using a Hybrid Data Association Model}},
author = {Baum, Marcus and Willett, Peter and Bar-Shalom, Yaakov and Hanebeck, Uwe D.},
booktitle = {SPIE - Signal and Data Processing of Small Targets 2014},
year = {2014},
doi = {10.1117/12.2053431}
}
2013
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The Kernel-SME Filter for Multiple Target Tracking
M. Baum and U. D. Hanebeck
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, 2013.
Abstract
BibTex
PDF
URL
We present a novel method for tracking multiple targets, called Kernel-SME filter, that does not require an enumeration of measurement-to-target associations. This method is a further development of the symmetric measurement equation (SME) filter that removes the data association uncertainty of the original measurement equation with the help of a symmetric transformation. The key idea of the Kernel-SME filter is to define a symmetric transformation that maps the measurements to a Gaussian mixture function. This transformation is scalable to a large number of targets and allows for deriving a Gaussian state estimator that only has a cubic runtime complexity in the number of targets.
@inproceedings{Fusion13_SME_Baum,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{The Kernel-SME Filter for Multiple Target Tracking}},
booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
year = {2013},
address = {Istanbul, Turkey},
month = jul,
file = {Fusion13_Baum.pdf:http\://isas.iar.kit.edu/pdf/Fusion13_Baum.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6641290/}
}
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Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds
F. Faion, M. Baum, and U. D. Hanebeck
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, 2013.
Abstract
BibTex
PDF
URL
In this paper, we consider the problem of jointly tracking the pose and shape of objects based on noisy data from cameras and depth sensors. Our proposed approach formalizes object silhouettes from image data as measurements within a Bayesian estimation framework. Projecting object silhouettes from images back into space yields a visual hull that constrains the object. In this work, we focus on the 2D case. We derive a general equation for the silhouette measurement update that explicitly considers segmentation uncertainty of each pixel. By assuming a bounded error for the silhouettes, we can reduce the complexity of the general solution to only consider uncertain edges and derive an approximate measurement update. In simulations, we show that the proposed approach dramatically improves point-cloud-based estimators, especially in the presence of high noise.
@inproceedings{Fusion13_Faion,
author = {Faion, Florian and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds}},
booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
year = {2013},
address = {Istanbul, Turkey},
month = jul,
file = {Fusion13_Faion.pdf:http\://isas.iar.kit.edu/pdf/Fusion13_Faion.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6641247/}
}
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Level-Set Random Hyper Surface Models for Tracking Complex Extended Objects
A. Zea, F. Faion, M. Baum, and U. D. Hanebeck
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, 2013.
Abstract
BibTex
PDF
This paper presents a novel approach to track a non-convex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of Random Hypersurface Model (RHM), called Level-Set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-Set RHM, a nonlinear measurement equation can be derived that allows to employ a standard Gaussian state estimator for tracking an extended object even in scenarios with high measurement noise. In this paper, shapes are described using polygons and shape regularization is applied using ideas from active contour models.
@inproceedings{Fusion13_Zea,
title = {{Level-Set Random Hyper Surface Models for Tracking Complex Extended Objects}},
author = {Zea, Antonio and Faion, Florian and Baum, Marcus and Hanebeck, Uwe D.},
booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
year = {2013},
address = {Istanbul, Turkey},
month = jul,
file = {Fusion13_Zea.pdf:http\://isas.iar.kit.edu/pdf/Fusion13_Zea.pdf:PDF}
}
2012
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Optimal Point Estimates for Multi-target States based on Kernel Distances
M. Baum, P. Ruoff, D. Itte, and U. D. Hanebeck
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, 2012.
Abstract
BibTex
PDF
DOI
Almost all multi-target tracking systems have to generate point estimates for the targets, e.g., for displaying the tracks. The novel idea in this paper is to consider point estimates for multi-target states that are optimal according to a kernel distance measure. Because the kernel distance is a metric on point sets and ignores the target labels, shortcomings of Minimum Mean Squared Error (MMSE) estimates for multi-target states can be avoided. We show how the calculation of these point estimates can be casted as an optimization problem and it turns out that it corresponds to the problem of reducing the Probability Hypothesis Density (PHD) function to a Dirac mixture density. Finally, we discuss a generalization of the kernel distance called LCD distance, which does not require to choose a specific kernel width. The presented methods are evaluated in a Multiple-Hypotheses Tracker (MHT) setting with up to ten targets.
@inproceedings{CDC12_Baum,
author = {Baum, Marcus and Ruoff, Patrick and Itte, Dominik and Hanebeck, Uwe D.},
title = {{Optimal Point Estimates for Multi-target States based on Kernel Distances}},
booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
year = {2012},
address = {Maui, Hawaii, USA},
month = dec,
doi = {10.1109/CDC.2012.6426189},
file = {CDC12_Baum.pdf:http\://isas.iar.kit.edu/pdf/CDC12_Baum.pdf:PDF}
}
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Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion
M. Baum and U. D. Hanebeck
IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 4, Oct. 2012.
Abstract
BibTex
DOI
A novel approach for extended object tracking is presented. In contrast to existing approaches, no statistical assumptions about the location of the measurement sources on the extended target object are made. As a consequence, a combined set-theoretic and stochastic estimator is obtained that is robust to systematic errors in the target model. The benefits of the new approach is demonstrated by means of simulations.
@article{J_TAES_Baum,
title = {{Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion}},
author = {Baum, Marcus and Hanebeck, Uwe D.},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
year = {2012},
month = oct,
number = {4},
pages = {3103-3115},
volume = {48},
doi = {10.1109/TAES.2012.6324680},
issn = {0018-9251}
}
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Tracking Ground Moving Extended Objects using RGBD Data
M. Baum, F. Faion, and U. D. Hanebeck
Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012), Hamburg, Germany, 2012.
Best Student Paper Award Finalist
Abstract
BibTex
PDF
DOI
This paper is about an experimental set-up for tracking a ground moving mobile object from a bird’s eye view. In this experiment, an RGB and depth camera is used for detecting moving points. The detected points serve as input for a probabilistic extended object tracking algorithm that simultaneously estimates the kinematic parameters and the shape parameters of the object. By this means, it is easy to discriminate moving objects from the background and the probabilistic tracking algorithm ensures a robust and smooth shape estimate. We provide an experimental evaluation of a recent Bayesian extended object tracking algorithm based on a so-called Random Hypersurface Model and give a comparison with active contour models.
@inproceedings{MFI12_Baum,
author = {Baum, Marcus and Faion, Florian and Hanebeck, Uwe D.},
title = {{Tracking Ground Moving Extended Objects using RGBD Data}},
booktitle = {Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)},
year = {2012},
address = {Hamburg, Germany},
month = sep,
note = {\textbf{\color{red}Best Student Paper Award Finalist}},
award = {Best Student Paper Award Finalist},
doi = {10.1109/MFI.2012.6343003},
file = {MFI12_Baum.pdf:http\://isas.iar.kit.edu/pdf/MFI12_Baum.pdf:PDF}
}
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Student Research Highlight: Simultaneous Tracking and Shape Estimation of Extended Targets
M. Baum
IEEE Aerospace and Electronic Systems Magazine, vol. 27, no. 7, Jul. 2012.
BibTex
DOI
@article{J_Baum2012,
author = {Baum, Marcus},
title = {{Student Research Highlight: Simultaneous Tracking and Shape Estimation of Extended Targets}},
journal = {IEEE Aerospace and Electronic Systems Magazine},
year = {2012},
volume = {27},
number = {7},
pages = {42-44},
month = jul,
issn = {0885-8985},
doi = {10.1109/MAES.2012.6328840}
}
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Modeling the Target Extent with Multiplicative Noise
M. Baum, F. Faion, and U. D. Hanebeck
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, 2012.
Abstract
BibTex
PDF
URL
Extended target tracking deals with simultaneously tracking the shape and the kinematic parameters of a target. In this work, we formulate the extended target tracking problem as a state estimation problem with both multiplicative and additive measurement noise. In case of extended targets with known orientation, we show that the best linear estimator is not consistent and, hence, is unsuitable for this problem. In order to overcome this issue, we propose a quadratic estimator for a recursive closed-form measurement update. Simulations demonstrate the performance of the estimator.
@inproceedings{Fusion12_Baum,
author = {Baum, Marcus and Faion, Florian and Hanebeck, Uwe D.},
title = {{Modeling the Target Extent with Multiplicative Noise}},
booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
year = {2012},
address = {Singapore},
month = jul,
file = {Fusion12_Baum-MultNoise.pdf:http\://isas.iar.kit.edu/pdf/Fusion12_Baum-MultNoise.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6290596/}
}
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Calculating Some Exact MMOSPA Estimates for Particle Distributions
M. Baum, P. Willett, and U. D. Hanebeck
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, 2012.
Abstract
BibTex
PDF
URL
In this work, we present some exact algorithms for calculating the minimum mean optimal sub-pattern assignment (MMOSPA) estimate for probability densities that are represented with particles. First, a polynomial-time algorithm for two targets is derived by reducing the problem to the enumeration of the cells of a hyperplane arrangement. Second, we present a linear-time algorithm for an arbitrary number of scalar target states, which is based on the insight that the MMOSPA estimate coincides with the mean of the order statistics.
@inproceedings{Fusion12_BaumWillett,
author = {Baum, Marcus and Willett, Peter and Hanebeck, Uwe D.},
title = {{Calculating Some Exact MMOSPA Estimates for Particle Distributions}},
booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
year = {2012},
address = {Singapore},
month = jul,
file = {Fusion12_Baum-MMOSPA.pdf:http\://isas.iar.kit.edu/pdf/Fusion12_Baum-MMOSPA.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6289890/}
}
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Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models
F. Faion, M. Baum, and U. D. Hanebeck
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, 2012.
Abstract
BibTex
PDF
URL
Depth sensors such as the Microsoft Kinect™depth sensor provide three dimensional point clouds of an observed scene. In this paper, we employ Random Hypersurface Models (RHMs), which is a modeling technique for extended object tracking, to point cloud fusion in order to track a shape approximation of an underlying object. We present a novel variant of RHMs to model shapes in 3D space. Based on this novel model, we develop a specialized algorithm to track persons by approximating their shapes as cylinders. For evaluation, we utilize a Kinect network and simulations based on a stochastic sensor model.
@inproceedings{Fusion12_Faion-CylinderTracking,
author = {Faion, Florian and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models}},
booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
year = {2012},
address = {Singapore},
month = jul,
file = {Fusion12_Faion-CylinderTracking.pdf:http\://isas.iar.kit.edu/pdf/Fusion12_Faion-CylinderTracking.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6290575/}
}
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Evaluation of Tracking Methods for Maritime Surveillance
Y. Fischer, M. Baum, F. Flohr, U. Hanebeck, and J. Beyerer
Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE), Baltimore, Maryland, USA, 2012.
Abstract
BibTex
DOI
In this article we present an evaluation of different target tracking methods based on various simulated scenarios in the maritime domain. We implemented well known algorithms (JIPDA, Linear Multi Target PDA, Linear Joint PDA, Monte Carlo Markov Chain Data Association) and integrated them into a data fusion architecture. The algorithms have been compared based on extensions of the Optimal Subpattern Assignment metric. Also further performance measures are used to get a single score for each algorithm. As no single algorithm is equally well fitted to all tested scenarios, our results show which algorithms fits best for specific scenarios.
@inproceedings{SPIE12_FischerBaum,
author = {Fischer, Yvonne and Baum, Marcus and Flohr, Fabian and Hanebeck, Uwe and Beyerer, J\"urgen},
booktitle = {Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE)},
title = {{Evaluation of Tracking Methods for Maritime Surveillance}},
year = {2012},
address = {Baltimore, Maryland, USA},
month = apr,
doi = {10.1117/12.919234},
url = {http://dx.doi.org/10.1117/12.919234}
}
2011
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Random Hypersurface Mixture Models for Tracking Multiple Extended Objects
M. Baum, B. Noack, and U. D. Hanebeck
Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, 2011.
Abstract
BibTex
PDF
DOI
This paper presents a novel method for tracking multiple extended objects. The shape of a single extended object is modeled with a recently developed approach called Random Hypersurface Model (RHM) that assumes a varying number of measurement sources to lie on scaled versions of the shape boundaries. This approach is extended by introducing a so-called Mixture Random Hypersurface Model (Mixture RHM), which allows for modeling multiple extended targets. Based on this model, a Gaussian-assumed Bayesian tracking method that provides the means to track and estimate shapes of multiple extended targets is derived. Simulations demonstrate the performance of the new approach.
@inproceedings{CDC11_Baum,
author = {Baum, Marcus and Noack, Benjamin and Hanebeck, Uwe D.},
title = {{Random Hypersurface Mixture Models for Tracking Multiple Extended Objects}},
booktitle = {Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)},
year = {2011},
address = {Orlando, Florida, USA},
month = dec,
doi = {10.1109/CDC.2011.6161522},
file = {CDC11_Baum.pdf:http\://isas.iar.kit.edu/pdf/CDC11_Baum.pdf:PDF}
}
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Fitting Conics to Noisy Data Using Stochastic Linearization
M. Baum and U. D. Hanebeck
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, 2011.
Abstract
BibTex
PDF
DOI
Fitting conic sections, e.g., ellipses or circles, to noisy data points is a fundamental sensor data processing problem, which frequently arises in robotics. In this paper, we introduce a new procedure for deriving a recursive Gaussian state estimator for fitting conics to data corrupted by additive Gaussian noise. For this purpose, the original exact implicit measurement equation is reformulated with the help of suitable approximations as an explicit measurement equation corrupted by multiplicative noise. Based on stochastic linearization, an efficient Gaussian state estimator is derived for the explicit measurement equation. The performance of the new approach is evaluated by means of a typical ellipse fitting scenario.
@inproceedings{IROS11_Baum,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Fitting Conics to Noisy Data Using Stochastic Linearization}},
booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
year = {2011},
address = {San Francisco, California, USA},
month = sep,
doi = {10.1109/IROS.2011.6094982},
file = {IROS11_Baum.pdf:http\://isas.iar.kit.edu/pdf/IROS11_Baum.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/6094982/}
}
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Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
B. Noack, M. Baum, and U. D. Hanebeck
Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, 2011.
Abstract
BibTex
PDF
Especially in the field of sensor networks and multi-robot systems, fully decentralized estimation techniques are of particular interest. As the required elimination of the complex dependencies between estimates generally yields inconsistent results, several approaches, e.g., covariance intersection, maintain consistency by providing conservative estimates. Unfortunately, these estimates are often too conservative and therefore, much less informative than a corresponding centralized approach. In this paper, we provide a concept that conservatively decorrelates the estimates while bounding the unknown correlations as closely as possible. For this purpose, known independent quantities, such as measurement noise, are explicitly identified and exploited. Based on tight covariance bounds, the new approach allows for an intuitive and systematic derivation of appropriate tailor-made filter equations and does not require heuristics. Its performance is demonstrated in a comparative study within a typical SLAM scenario.
@inproceedings{2011_IFAC_Noack,
author = {Noack, Benjamin and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation}},
booktitle = {Proceedings of the 18th IFAC World Congress (IFAC 2011)},
year = {2011},
address = {Milan, Italy},
month = aug,
file = {IFAC11_Noack.pdf:http\://isas.iar.kit.edu/pdf/IFAC11_Noack.pdf:PDF}
}
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Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs
M. Baum and U. D. Hanebeck
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 2011.
Best Student Paper Award
Abstract
BibTex
PDF
DOI
Source Code
This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHM is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.
@inproceedings{Fusion11_Baum-RHM,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs}},
booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
year = {2011},
address = {Chicago, Illinois, USA},
month = jul,
award = {Best Student Paper Award},
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/RandomHypersurfaceModel},
doi = {https://ieeexplore.ieee.org/document/5977661/},
file = {Fusion11_Baum-RHM.pdf:http\://isas.iar.kit.edu/pdf/Fusion11_Baum-RHM.pdf:PDF},
note_alt = {\textbf{\color{red}Best Student Paper Award}}
}
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Using Symmetric State Transformations for Multi-Target Tracking
M. Baum and U. D. Hanebeck
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 2011.
Abstract
BibTex
PDF
URL
This paper is about the use of symmetric state transformations for multi-target tracking. First, a novel method for obtaining point estimates for multi-target states is proposed. The basic idea is to apply a symmetric state transformation to the original state in order to compute a minimum mean-square-error (MMSE) estimate in a transformed state. By this means, the known shortcomings of MMSE estimates for multi-target states can be avoided. Second, a new multi-target tracking method based on state transformations is suggested, which entirely performs the time and measurement update in a transformed space and thus, avoids the explicit calculation of data association hypotheses and removes the target identity from the estimation problem. The performance of the new approach is evaluated by means of tracking two crossing targets.
@inproceedings{Fusion11_Baum-USF,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Using Symmetric State Transformations for Multi-Target Tracking}},
booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
year = {2011},
address = {Chicago, Illinois, USA},
month = jul,
file = {Fusion11_Baum-USF.pdf:http\://isas.iar.kit.edu/pdf/Fusion11_Baum-USF.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/5977702/}
}
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Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
M. Baum, B. Noack, F. Beutler, D. Itte, and U. D. Hanebeck
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 2011.
Abstract
BibTex
PDF
URL
This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typical multiple target tracking scenarios.
@inproceedings{Fusion11_Baum,
author = {Baum, Marcus and Noack, Benjamin and Beutler, Frederik and Itte, Dominik and Hanebeck, Uwe D.},
title = {{Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking}},
booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
year = {2011},
address = {Chicago, Illinois, USA},
month = jul,
file = {Fusion11_Baum.pdf:http\://isas.iar.kit.edu/pdf/Fusion11_Baum.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/5977706/}
}
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Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities
B. Noack, M. Baum, and U. D. Hanebeck
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 2011.
BibTex
PDF
URL
@inproceedings{2011_FUSION_Noack,
author = {Noack, Benjamin and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities}},
booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
year = {2011},
address = {Chicago, Illinois, USA},
month = jul,
file = {Fusion11_Noack.pdf:http\://isas.iar.kit.edu/pdf/Fusion11_Noack.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/5977577/}
}
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Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations
M. Reinhardt, B. Noack, M. Baum, and U. D. Hanebeck
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 2011.
Abstract
BibTex
PDF
URL
In data fusion theory, multiple estimates are combined to yield an optimal result. In this paper, the set of all possible results is investigated, when two random variables with unknown correlations are fused. As a first step, recursive processing of the set of estimates is examined. Besides set-theoretic considerations, the lack of knowledge about the unknown correlation coefficient is modeled as a stochastic quantity. Especially, a uniform model is analyzed, which provides a new optimization criterion for the covariance intersection algorithm in scalar state spaces. This approach is also generalized to multi-dimensional state spaces in an approximative, but fast and scalable way, so that consistent estimates are obtained.
@inproceedings{Fusion11_Reinhardt,
author = {Reinhardt, Marc and Noack, Benjamin and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations}},
booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
year = {2011},
address = {Chicago, Illinois, USA},
month = jul,
file = {Fusion11_Reinhardt.pdf:http\://isas.iar.kit.edu/pdf/Fusion11_Reinhardt.pdf:PDF},
url = {https://ieeexplore.ieee.org/document/5977580/}
}
2010
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Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models
M. Baum, M. Feldmann, D. Fränken, U. D. Hanebeck, and W. Koch
Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010), Leipzig, Germany, 2010.
Abstract
BibTex
PDF
Based on previous work of the authors, this paper provides a comparison of two different tracking methodologies for extended objects and group targets, where the true shape of the extent is approximated by an ellipsoid. Although both methods exploit usual sensor data, i.e., position measurements of varying scattering centers, the distinctions are a consequence of the different modeling of the extent as a symmetric positive definite random matrix on the one hand and an elliptic random hypersurface model on the other. Besides analyzing the fundamental assumptions and a comparison of the properties of these tracking methods, simulation results are presented based on a static tracking environment to highlight especially the differences in the update step for the extension estimate.
@inproceedings{SDF10_Baum,
title = {{Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models}},
author = {Baum, Marcus and Feldmann, Michael and Fr{\"{a}}nken, Dietrich and Hanebeck, Uwe D. and Koch, Wolfgang},
booktitle = {Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010)},
year = {2010},
address = {Leipzig, Germany},
month = oct,
file = {SDF10_BaumFeldmann.pdf:http\://isas.iar.kit.edu/pdf/SDF10_BaumFeldmann.pdf:PDF}
}
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Data Association in a World Model for Autonomous Systems
M. Baum, I. Gheta, A. Belkin, J. Beyerer, and U. D. Hanebeck
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, 2010.
Abstract
BibTex
PDF
DOI
This contribution introduces a three pillar information storage and management system for modeling the environment of autonomous systems. The main characteristics is the separation of prior knowledge, environment model and sensor information. In the center of the system is the environment model, which provides the autonomous system with information about the current state of the environment. It consists of instances with attributes and relations as virtual substitutes of entities (persons and objects) of the real world. Important features are the representation of uncertain information by means of Degree-of-Belief (DoB) distributions, the information exchange between the three pillars as well as creation, deletion and update of instances, attributes and relations in the environment model. In this work, a Bayesian method for fusing new observations to the environment model is introduced. For this purpose, a Bayesian data association method is derived. The main question answered here is the observation-to-instance mapping and the decision mechanisms for creating a new instance or updating already existing instances in the environment model.
@inproceedings{MFI10_BaumGheta,
author = {Baum, Marcus and Gheta, Ioana and Belkin, Andrey and Beyerer, J{\"u}rgen and Hanebeck, Uwe D.},
title = {{Data Association in a World Model for Autonomous Systems}},
booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
year = {2010},
address = {Salt Lake City, Utah, USA},
month = sep,
doi = {10.1109/MFI.2010.5604454},
file = {MFI10_BaumGheta.pdf:http\://isas.iar.kit.edu/pdf/MFI10_BaumGheta.pdf:PDF}
}
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Association-free Tracking of Two Closely Spaced Targets
M. Baum and U. D. Hanebeck
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, 2010.
Abstract
BibTex
PDF
DOI
This paper introduces a new concept for tracking closely spaced targets in Cartesian space based on position measurements corrupted with additive Gaussian noise. The basic idea is to select a special state representation that eliminates the target identity and avoids the explicit evaluation of association probabilities. One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal. Hence, it is especially suitable for closely spaced targets. The resulting estimation problem can be tackled with a standard nonlinear estimator. In this work, we focus on two targets in two-dimensional Cartesian space. The Cartesian coordinates of the targets are represented by means of extreme values, i.e., minima and maxima. Simulation results demonstrate the feasibility of the new approach.
@inproceedings{MFI10_Baum,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Association-free Tracking of Two Closely Spaced Targets}},
booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
year = {2010},
address = {Salt Lake City, Utah, USA},
month = sep,
doi = {10.1109/MFI.2010.5604450},
file = {MFI10_Baum.pdf:http\://isas.iar.kit.edu/pdf/MFI10_Baum.pdf:PDF}
}
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Tracking a Minimum Bounding Rectangle based on Extreme Value Theory
M. Baum and U. D. Hanebeck
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, 2010.
Abstract
BibTex
PDF
DOI
In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise. Extreme value theory is applied in order to derive a linear measurement equation for the problem. The new estimator is applied to the problem of group target and extended object tracking. Instead of estimating each single group member or point feature explicitly, the basic idea is to track a summarizing shape, namely the minimum bounding rectangle, of the group. Simulation results demonstrate the feasibility of the estimator.
@inproceedings{MFI10_BaumRect,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Tracking a Minimum Bounding Rectangle based on Extreme Value Theory}},
booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
year = {2010},
address = {Salt Lake City, Utah, USA},
month = sep,
doi = {10.1109/MFI.2010.5604456},
file = {MFI10_BaumRect.pdf:http\://isas.iar.kit.edu/pdf/MFI10_BaumRect.pdf:PDF}
}
-
Three Pillar Information Management System for Modeling the Environment of Autonomous Systems
I. Gheta, M. Baum, A. Belkin, J. Beyerer, and U. D. Hanebeck
Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010), Taranto, Italy, 2010.
Abstract
BibTex
PDF
DOI
This contribution is about an information management and storage system for modeling the environment of autonomous systems. The three pillars of the system consist of prior knowledge, environment model and sensory information. The main pillar is the environment model, which supplies the autonomous system with relevant information about its current environment. For this purpose, an abstract representation of the real world is created, where instances with attributes and relations serve as virtual substitutes of entities (persons and objects) of the real world. The environment model is created based on sensory information about the real world. The gathered sensory information is typically uncertain in a stochastic sense and is represented in the environment model by means of Degree-of-Belief (DoB) distributions. The prior knowledge contains all relevant background knowledge (e.g., concepts organized in ontologies) for creating and maintaining the environment model. The concept of the three pillar information system has previously been published. Therefore this contribution focuses on further central properties of the system. Furthermore, the development status and possible applications as well as evaluation scenarios are discussed.
@inproceedings{VECIMS10_Baum,
author = {Gheta, Ioana and Baum, Marcus and Belkin, Andrey and Beyerer, J{\"{u}}rgen and Hanebeck, Uwe D.},
title = {{Three Pillar Information Management System for Modeling the Environment of Autonomous Systems}},
booktitle = {Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010)},
year = {2010},
address = {Taranto, Italy},
month = sep,
doi = {10.1109/MFI.2010.5604454},
file = {VECIMS10_GhetaBaum.pdf:http\://isas.iar.kit.edu/pdf/VECIMS10_GhetaBaum.pdf:PDF}
}
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A Visual Interactive Debugger Based on Symbolic Execution
R. Hähnle, M. Baum, R. Bubel, and M. Rothe
Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010), Antwerp, Belgium, 2010.
BibTex
DOI
@inproceedings{ASE10_Baum,
author = {H\"{a}hnle, Reiner and Baum, Marcus and Bubel, Richard and Rothe, Marcel},
title = {{A Visual Interactive Debugger Based on Symbolic Execution}},
booktitle = {Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010)},
year = {2010},
address = {Antwerp, Belgium},
month = sep,
doi = {10.1145/1858996.1859022}
}
-
A Novel Bayesian Method for Fitting a Circle to Noisy Points
M. Baum, V. Klumpp, and U. D. Hanebeck
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 2010.
Abstract
BibTex
PDF
DOI
This paper introduces a novel recursive Bayesian estimator for the center and radius of a circle based on noisy points. Each given point is assumed to be a noisy measurement of an unknown true point on the circle that is corrupted with known isotropic Gaussian noise. In contrast to existing approaches, the novel method does not make assumptions about the true points on the circle, where the measurements stem from. Closed-form expressions for the measurement update step are derived. Simulations show that the novel method outperforms standard Bayesian approaches for circle fitting.
@inproceedings{Fusion10_BaumKlumpp,
author = {Baum, Marcus and Klumpp, Vesa and Hanebeck, Uwe D.},
booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
title = {{A Novel Bayesian Method for Fitting a Circle to Noisy Points}},
year = {2010},
address = {Edinburgh, United Kingdom},
month = jul,
doi = {10.1109/ICIF.2010.5711884},
file = {Fusion10_BaumKlumpp.pdf:http\://isas.iar.kit.edu/pdf/Fusion10_BaumKlumpp.pdf:PDF}
}
-
Extended Object and Group Tracking with Elliptic Random Hypersurface Models
M. Baum, B. Noack, and U. D. Hanebeck
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 2010.
Abstract
BibTex
PDF
DOI
This paper provides new results and insights for tracking an extended target object modeled with an Elliptic Random Hypersurface Model (RHM). An Elliptic RHM specifies the relative squared Mahalanobis distance of a measurement source to the center of the target object by means of a one-dimensional random scaling factor. It is shown that uniformly distributed measurement sources on an ellipse lead to a uniformly distributed squared scaling factor. Furthermore, a Bayesian inference mechanisms tailored to elliptic shapes is introduced, which is also suitable for scenarios with high measurement noise. Closed-form expressions for the measurement update in case of Gaussian and uniformly distributed squared scaling factors are derived.
@inproceedings{Fusion10_BaumNoack,
author = {Baum, Marcus and Noack, Benjamin and Hanebeck, Uwe D.},
title = {{Extended Object and Group Tracking with Elliptic Random Hypersurface Models}},
booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
year = {2010},
address = {Edinburgh, United Kingdom},
month = jul,
doi = {10.1109/ICIF.2010.5711854},
file = {Fusion10_BaumNoack.pdf:http\://isas.iar.kit.edu/pdf/Fusion10_BaumNoack.pdf:PDF}
}
-
Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter
V. Klumpp, B. Noack, M. Baum, and U. D. Hanebeck
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 2010.
Abstract
BibTex
PDF
DOI
In estimation theory, mainly set-theoretic or stochastic uncertainty is considered. In some cases, especially when some statistics of a distribution are not known or additional stochastic information is used in a set-theoretic estimator, both types of uncertainty have to be considered. In this paper, two estimators that cope with combined stoachastic and set-theoretic uncertainty are compared, namely the Set-theoretic and Statistical Information filter, which represents the uncertainty by means of random sets, and the Credal State filter, in which the state information is given by sets of probability density functions. The different uncertainty assessment in both estimators leads to different estimation results, even when the prior information and the measurement and system models are equal. This paper explains these differences and states directions, when which estimator should be applied to a given estimation problem.
@inproceedings{Fusion10_Klumpp,
author = {Klumpp, Vesa and Noack, Benjamin and Baum, Marcus and Hanebeck, Uwe D.},
title = {{Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter}},
booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
year = {2010},
address = {Edinburgh, United Kingdom},
month = jul,
doi = {10.1109/ICIF.2010.5711908},
file = {Fusion10_Klumpp.pdf:http\://isas.iar.kit.edu/pdf/Fusion10_Klumpp.pdf:PDF}
}
2009
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Random Hypersurface Models for Extended Object Tracking
M. Baum and U. D. Hanebeck
Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009), Ajman, United Arab Emirates, 2009.
Abstract
BibTex
PDF
DOI
Target tracking algorithms usually assume that the received measurements stem from a point source. However, in many scenarios this assumption is not feasible so that measurements may stem from different locations, named measurement sources, on the target surface. Then, it is necessary to incorporate the target extent into the estimation procedure in order to obtain robust and precise estimation results. This paper introduces the novel concept of Random Hypersurface Models for extended targets. A Random Hypersurface Model assumes that each measurement source is an element of a randomly generated hypersurface. The applicability of this approach is demonstrated by means of an elliptic target shape. In this case, a Random Hypersurface Model specifies the random (relative) Mahalanobis distance of a measurement source to the center of the target object. As a consequence, good estimation results can be obtained even if the true target shape significantly differs from the modeled shape. Additionally, Random Hypersurface Models are computationally tractable with standard nonlinear stochastic state estimators.
@inproceedings{ISSPIT09_Baum,
author = {Baum, Marcus and Hanebeck, Uwe D.},
title = {{Random Hypersurface Models for Extended Object Tracking}},
booktitle = {Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009)},
year = {2009},
address = {Ajman, United Arab Emirates},
month = dec,
doi = {10.1109/ISSPIT.2009.5407526},
file = {ISSPIT09_Baum.pdf:http\://isas.iar.kit.edu/pdf/ISSPIT09_Baum.pdf:PDF}
}
-
Tracking an Extended Object Modeled as an Axis-Aligned Rectangle
M. Baum and U. D. Hanebeck
4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft für Informatik e.V. (GI), Lübeck, Germany, 2009.
Abstract
BibTex
PDF
In many tracking applications, the extent of the target object is neglected and it is assumed that the received measurements stem from a point source. However, modern sensors are able to supply several measurements from different scattering cen- ters on the target object due to their high-resolution capability. As a consequence, it becomes necessary to incorporate the target extent into the estimation procedure. This paper introduces a new method for tracking the smallest enclosing rectangle of an ex- tended object with an unknown shape. At each time step, a finite set of noisy position measurements that stem from arbitrary, unknown measurement sources on the target surface may be available. In contrast to common approaches, the presented approach does not have to make any statistical assumptions on the measurement sources.
@inproceedings{SDF09_Baum,
title = {{Tracking an Extended Object Modeled as an Axis-Aligned Rectangle}},
author = {Baum, Marcus and Hanebeck, Uwe D.},
booktitle = {4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft f\"ur Informatik e.V. (GI)},
year = {2009},
address = {L{\"u}beck, Germany},
month = oct,
file = {SDF09_Baum.pdf:http\://isas.iar.kit.edu/pdf/SDF09_Baum.pdf:PDF}
}
-
Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
M. Baum and U. D. Hanebeck
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, 2009.
Abstract
BibTex
PDF
In this paper, a novel approach for tracking extended objects is presented. The target object is modeled as a circular disc such that the center and extent of the target object can be estimated. At each time step, a finite set of position measurements that are corrupted with stochastic noise may be available. Each position measurement stems from an unknown measurement source on the extended object. In contrast to existing approaches, no statistical assumptions about the distribution of the measurement sources on the extended object are made. As a consequence, it is necessary to deal with stochastic and set-valued uncertainties. For this purpose, a novel combined stochastic and set-theoretic estimator that employs random hyperboloids to express the uncertainties about the true circular disc is derived.
@inproceedings{Fusion09_Baum,
title = {{Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion}},
author = {Baum, Marcus and Hanebeck, Uwe D.},
booktitle = {Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)},
year = {2009},
address = {Seattle, Washington, USA},
month = jul,
file = {Fusion09_Baum.pdf:http\://isas.iar.kit.edu/pdf/Fusion09_Baum.pdf:PDF}
}