The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
MCMC-based particle filtering for tracking a variable number of interacting targets
Abstract
We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.
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Cites
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
GREEN, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,930
Design and Analysis of Modern Tracking Systems
Blackman, S. S., & Populi, R. (1999) · Cited by 3,509
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Monte Carlo localization for mobile robots
Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (2003) · Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C) · doi:10.1109/robot.1999.772544 · Cited by 1,620
Self-citationSonar tracking of multiple targets using joint probabilistic data association
Fortmann, T., Bar-Shalom, Y., & Scheffe, M. (1983) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.1983.1145560 · Cited by 1,460
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Monocular Pedestrian Detection: Survey and Experiments
Enzweiler, M., & Gavrila, D. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.260 · Cited by 1,213
Multiple Object Tracking Using K-Shortest Paths Optimization
Berclaz, J., Fleuret, F., Turetken, E., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.21 · Cited by 1,015
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Branson, K., Robie, A. A., Bender, J., Perona, P., & Dickinson, M. H. (2009) · Nature Methods · doi:10.1038/nmeth.1328 · Cited by 786
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., & Lyu, S. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102907 · Cited by 804
Multiple Hypothesis Tracking Revisited
Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.533 · Cited by 666
Metadata
- Authors
- Khan, Z., Balch, T., & Dellaert, F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-11
- Volume
- 27
- Issue
- 11
- Pages
- 1805-1819
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2113926057
- Cited by
- 771
- References
- 40
- FWCI
- 45.35
- Type
- journal article
- Language
- en
- Open access
- closed
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