Latent data association: Bayesian model selection for multi-target tracking

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 2904-2911

  Segal, Aleksandr V ^a   Reid, Ian ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

bayesian inference; data association; hybrid inference; message passing; model selection; multi target tracking

Indexed keywords

BAYESIAN NETWORKS; DATA PROCESSING; INFERENCE ENGINES; LINEAR CONTROL SYSTEMS; MESSAGE PASSING;

BAYESIAN INFERENCE; DATA ASSOCIATION; HYBRID INFERENCE; MODEL SELECTION; MULTI-TARGET TRACKING;

TARGET TRACKING;

EID: 84898786107     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.361     Document Type: Conference Paper

References (27)

1. M. Andriluka, S. Roth, and B. Schiele. People-tracking-bydetection and people-detection-by-tracking. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 2008.
2. A. Andriyenko and K. Schindler. Globally optimal multitarget tracking on a hexagonal lattice. In European Conference on Computer Vision (ECCV), volume 6311 of Lecture Notes in Computer Science, pages 466-479. Springer Berlin Heidelberg, 2010.
3. A. Andriyenko and K. Schindler. Multi-target tracking by continuous energy minimization. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, pages 1265-1272, 2011.
4. A. Andriyenko, K. Schindler, and S. Roth. Discretecontinuous optimization for multi-target tracking. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, pages 1926-1933, 2012.
5. Y. Bar-Shalom and E. Tse. Tracking in a cluttered environment with probabilistic data association. Automatica, 11(5):451-460, 1975.
6. B. Benfold and I. Reid. Stable multi-target tracking in real-time surveillance video. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, pages 3457-3464, 2011.
7. J. Berclaz, F. Fleuret, E. Turetken, and P. Fua. Multiple Object Tracking Using K-Shortest Paths Optimization. volume 33, pages 1806-1819, 2011.
8. K. Bernardin and R. Stiefelhagen. Evaluating multiple object tracking performance: The clear mot metrics. EURASIP Journal on Image and Video Processing, 2008(1):246309, 2008.
9. M. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. Van Gool. Online multiperson tracking-by-detection from a single, uncalibrated camera. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(9):1820-1833, 2011.
10. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, volume 1, pages 886-893 vol. 1, 2005.
11. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 2008.
12. J. Ferryman, A. Shahrokni, et al. An overview of the PETS 2009 challenge. 2009.
13. T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe. Sonar tracking of multiple targets using joint probabilistic data association. Oceanic Engineering, IEEE Journal of, 8(3):173-184, 1983.
14. H. Jiang, S. Fels, and J. Little. A Linear Programming Approach for Multiple Object Tracking. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 2007.
15. Z. Khan, T. Balch, and F. Dellaert. An mcmc-based particle filter for tracking multiple interacting targets. In European Conference on Computer Vision (ECCV), volume 3024 of Lecture Notes in Computer Science, pages 279-290. Springer Berlin Heidelberg, 2004.
16. B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool. Coupled object detection and tracking from static cameras and moving vehicles. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(10):1683-1698, 2008.
17. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, volume 1, pages 878-885 vol. 1, 2005.
18. E. Maggio, M. Taj, and A. Cavallaro. Efficient multitarget visual tracking using random finite sets. Circuits and Systems for Video Technology, IEEE Transactions on, 18(8):1016-1027, 2008.
19. K. Murphy. Switching kalman filters. Technical report, Citeseer, 1998.
20. S. Oh, S. Russell, and S. Sastry. Markov chain monte carlo data association for general multiple-target tracking problems. In Decision and Control, 2004. CDC. 43rd IEEE Conference on, volume 1, pages 735-742 Vol.1, 2004.
21. V. Prisacariu and I. Reid. fastHOG-a real-time GPU implementation of HOG. Technical Report 09, 2009.
22. D. Reid. An algorithm for tracking multiple targets. Automatic Control, IEEE Transactions on, 24(6):843-854, 1979.
23. A. Roshan Zamir, A. Dehghan, and M. Shah. Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs. In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, pages 343-356. Springer Berlin Heidelberg, 2012.
24. D. Schulz, W. Burgard, D. Fox, and A. Cremers. Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In Robotics and Automation, IEEE International Conference on, volume 2, pages 1665-1670 vol.2, 2001.
25. J. Vermaak, S. Godsill, and P. Perez. Monte carlo filtering for multi target tracking and data association. Aerospace and Electronic Systems, IEEE Transactions on, 41(1):309-332, 2005.
26. B.-N. Vo, S. Singh, and A. Doucet. Sequential monte carlo methods for multitarget filtering with random finite sets. Aerospace and Electronic Systems, IEEE Transactions on, 41(4):1224-1245, 2005.
27. L. Zhang, Y. Li, and R. Nevatia. Global data association for multi-object tracking using network flows. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 2008.