@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)}
}
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.
@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
}
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.
@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}
}
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.
@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}
}
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.
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.
@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}
}
@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}
}
Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths
This article considers the fusion of target estimates stemming from multiple sensors, where the spatial extent of the targets is incorporated. The target estimates are represented as ellipses parameterized with center orientation and semi-axis lengths and width. Here, the fusion faces challenges such as ambiguous parameterization and an unclear meaning of the Euclidean distance between such estimates. We introduce a novel Bayesian framework for random ellipses based on the concept of a Minimum Mean Gaussian Wasserstein (MMGW) estimator. The MMGW estimate is optimal with respect to the Gaussian Wasserstein (GW) distance, which is a suitable distance metric for ellipses. We develop practical algorithms to approximate the MMGW estimate of the fusion result. The key idea is to approximate the GW distance with a modified version of the Square Root (SR) distance. By this means, optimal estimation and fusion can be performed based on the square root of the elliptic shape matrices. We analyze different implementations using, e.g., Monte Carlo methods, and evaluate them in simulated scenarios. An extensive comparison with state-of-the-art methods highlights the benefits of estimators tailored to the Gaussian Wasserstein distances.
@article{2019_TR_Thormann,
author = {Thormann and Baum, Marcus},
title = {Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths},
journal = {TechRxiv preprint},
year = {2019},
month = dec,
archiveprefix = {TechRxiv},
code = {https://github.com/Fusion-Goettingen/ExtendedObjectTracking/tree/master/EllipseFusion},
doi = {10.36227/techrxiv.11336567.v1},
file = {:https\://www.techrxiv.org/ndownloader/files/20098721:PDF},
publisher = {TechRxiv},
url = {https://www.techrxiv.org/articles/Fusion_of_Elliptical_Extended_Object_Estimates_Parameterized_with_Orientation_and_Axes_Lengths/11336567/1}
}
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.
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}
}
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.
@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.
@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
}
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.
@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}
}
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.
@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}
}
@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,
doi = {10.1109/MFI49285.2020.9235211},
url = {https://www.techrxiv.org/articles/Deterministic_Gibbs_Sampling_for_Data_Association_in_Multi-Object_Tracking/12435398/1}
}
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.
@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}
}
@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}
}
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.
@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}
}
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.
@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}
}
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.
@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
}
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.
@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}
}
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.
@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}
}
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.
@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.
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/}
}
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.
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/}
}
External PhD Students
Jaya Shradha Fowdur, M. Sc.
Affiliation
German Aerospace Center (DLR), Institute of Communications and Navigation, Nautical Systems, Neustrelitz
