An online approach: Learning-semantic-scene-by-tracking and tracking-by-learning-semantic-scene

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 739-746

  Song, Xuan ^a   Shao, Xiaowei ^b   Zhao, Huijing ^a   Cui, Jinshi ^a   Shibasaki, Ryosuke ^b   Zha, Hongbin ^a  

^a Peking University   (China)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY ANALYSIS; ADAPTIVE LEARNING; HIGH DENSITY; OBJECT CLASSIFICATION; ONLINE UNSUPERVISED LEARNING; ROBUST TRACKING; SCENE STRUCTURE; TRACKING LOOP;

COMPUTER VISION; SUBWAY STATIONS;

SEMANTICS;

EID: 77955992287     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540143     Document Type: Conference Paper

References (33)

1. S. Ali and M. Shah. Floor fields for tracking in high density crowd scenes. Proc. ECCV, pages 1-14, 2008.
2. Y. Bar-Shalom and T. E. Fortmann. Tracking and data association. New York: Academic Press, 1998.
3. M. Betke, D. Hirsh, A. Bagchi, N. Hristov, and N. Makris. Tracking large variable numbers of objects in clutter. Proc. IEEE CVPR, pages 1180-1187, 2007.
4. B. Bose, X. Wang, and E. Grimson. Multi-class object tracking algorithm that handles fragmentation and grouping. Proc. IEEE CVPR, pages 1550-1557, 2007.
5. D. Comaniciu and P. Meer. Distribution free decomposition of multivariate data. Pattern Analysis and Applications, 38:22-30, 1999.
6. J. Cui, H. Zha, H. Zhao, and R.Shibasaki. Fusion of detection and matching based approaches for laser based multiple people tracking. Proc. IEEE CVPR, pages 642-649, 2006.
7. A. Doucet, S. J. Godsill, and C. Andrieu. On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(1):197-208, 2000.
8. Z. Fu, W. Hu, and T. Tan. Similarity based vehicle trajectory clustering and anomaly detection. Proc. IEEE International Conference on Image Processing, pages 1133-1136, 2005.
9. G. Gennari and G. Hager. Probabilistic data association methods in visual tracking of groups. Proc. IEEE CVPR, pages 876-881, 2004.
10. J. Harris. Algebraic geometry: A first course. Springer-Verlag, 1992.
11. H. Jiang, S. Fels, and J. Little. A linear programming approach for multiple object tracking. Proc. IEEE CVPR, pages 1380-1387, 2007.
12. J.Sullivan and S.Carlsson. Tracking and labeling of interacting multiple targets. Proc. ECCV, pages 661-675, 2006.
13. I. Junejo and H. Foroosh. Trajectory rectification and path modeling for video surveillance. Proc. IEEE ICCV, pages 230-237, 2007.
14. I. Junejo, O. Javed, and M. Shah. Multi feature path modeling for video surveillance. Proc. IEEE Conference on Pattern Recognition, pages 383- 386, 2004.
15. Z. Khan, T. Balch, and F. Dellaert. Mcmc data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Trans. on PAMI, 28(1):1960-1972, 2006.
16. O. Lanz and R. Manduchi. Hybrid joint-separable multibody tracking. Proc. IEEE CVPR, pages 413-420, 2005.
17. B. Leibe, K. Schindler, and L. Gool. Coupled detection and trajectory estimation for multi-object tracking. Proc. IEEE ICCV, pages 1110-1117, 2007.
18. D. Makris and T. Ellis. Automatic learning of an activitybased semantic scene model. Proc. IEEE Conference on Advanced Video and Signal Based Surveillance, pages 183-188, 2003.
19. P.Nillius, J.Sullivan, and S.Carlsson. Multi-target tracking - linking identities using bayesian network inference. Proc. IEEE CVPR, pages 2187-2194, 2006.
20. W. Qu, D. Schonfeld, and M. Mohamed. Real-time interactively distributed multi-object tracking using a magnetic-inertia potential model. Proc. IEEE ICCV, pages 535-540, 2005.
21. C. Rasmussen and G. Hager. Probabilistic data association methods for tracking complex visual objects. IEEE Trans. on PAMI, 23(1):560-576, 2001.
22. D. Read. An algorithm for tracking multiple targets. IEEE Trans. Automation and Control, 24(1):84-90, 1979.
23. D. Schulz, W. Burgard, D. Fox, and A. Cremers. People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research, 22(2):99-116, 2003.
24. X. Song, J. Cui, X. Wang, H. Zhao, and H. Zha. Tracking interacting targets with laser scanner via on-line supervised learning. Proc. IEEE International Conference on Robotics and Automation, pages 2271-2276, 2008.
25. J. Vermaak, S. Godsill, and P. Perez. Monte carlo filtering for multi target tracking and data association. IEEE Trans. Aerospace and Electronic Systems, 41(1):309-332, 2005.
26. R. Vidal. Online clustering of moving hyperplanes. Proc. Neural Information Processing Systems, pages 1433-1440, 2006.
27. X. Wang, K. Ma, G. Ng, and E. Grimson. Trajectory analysis and semantic region modeling using a nonparametric bayesian model. Proc. IEEE CVPR, pages 1-8, 2008.
28. X.Wang, K. Tieu, and E. Grimson. Learning semantic scene models by trajectory analysis. Proc. ECCV, pages 110-123, 2006.
29. M. Yang, T. Yu, and Y. Wu. Game-theoretic multiple target tracking. Proc. IEEE ICCV, pages 110-117, 2007.
30. A. Yilmaz, O. Javed, and M. Shah. Object tracking: A survey. ACM Computing Surveys, pages 3-47, 2006.
31. Q. Yu, G. Medioni, and I. Cohen. Multiple target tracking using spatio-temporal markov chain monte carlo data association. Proc. IEEE CVPR, pages 642-649, 2007.
32. H. Zhao and R.Shibasaki. A novel system for tracking pedestrians using multiple single-row laser range scanners. IEEE Transactions on Systems, Man and Cybernetics, part A, pages 283-291, 2005.
33. T. Zhao and R. Nevatia. Tracking multiple humans in complex situations. IEEE Trans. on PAMI, 7(1):1208-1221, 2004.