Tracking multiple objects through occlusion with online sampling and position estimation

Pattern Recognition

Volumn 41, Issue 8, 2008, Pages 2447-2460

  Zhu, Lin ^a   Zhou, Jie ^a   Song, Jingyan ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Multiple objects tracking; Occlusion; Online sampling; Position estimation

Indexed keywords

COLOR IMAGE PROCESSING; FEATURE EXTRACTION; OBJECT RECOGNITION; ONLINE SYSTEMS; POSITION MEASUREMENT;

MULTIPLE OBJECTS TRACKING; OCCLUSION; ONLINE SAMPLING;

TARGET TRACKING;

EID: 43049088785     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.01.014     Document Type: Article

References (27)

1. A. Amer, E. Dubois, A. Mitiche, Real-time system for high-level video representation: application to video surveillance, in: Conference on Visual Communication and Image Processing (VCIP), Proceedings of the SPIE International Symposium on Electronic Imaging, vol. 5022, Santa Clara, USA, January 2003, pp. 530-541.
2. R. Collins, A. Lipton, T. Kanada, et al., A system for video surveillance and monitoring: VSAM final report, Technical report CMU-RI-TR-00-12, Carnegie Mellon University, May 2000.
3. Haritaoglu I., Harwood D., and Davis L.S. W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22 8 (2000) 809-830
4. F. Lv, J. Kang, R. Nevatia, I. Cohen, G. Medioni, Automatic tracking and labeling of human activities in a video sequence, in: Proceedings of the 6th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS04), Prague, Czech Republic, May 2004.
5. Stauffer C., and Grimson W. Learning patterns of activity using real time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22 8 (2000) 747-767
6. Elgammal A.M., and Davis L.S. Probabilistic framework for segmenting people under occlusion. Proceedings of the 8th IEEE International Conference on Computer Vision (2001), IEEE Computer Society, Los Alamitos 145-152
7. Hotter M., and Thoma R. Image segmentation based on object oriented mapping parameter estimation. Signal Processing 15 3 (1988) 315-334
8. Lim K.P., Das A., and Chong M.N. Estimation of occlusion and dense motion fields in a bidirectional Bayesian framework. IEEE Trans. Pattern Anal. Mach. Intell. 24 5 (2002) 712-718
9. Zang Q., and Klette R. Object classification and tracking in video surveillance. Proceedings Computer Analysis of Images and Patterns, Lecture Notes in Computer Science vol. 2756 (2003), Springer, Berlin 198-205
10. Zhao T., and Nevatia R. Tracking multiple humans in crowded environment. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2 (2004), IEEE Computer Society 1I-406-II-413
11. Zhao T., and Nevatia R. Tracking multiple humans in complex situations. IEEE Trans. Pattern Anal. Mach. Intell. 26 9 (2004) 1208-1221
12. A. Senior, A. Hampapur, YL. Tian, L. Brown, S. Pankanti, R. Bolle, Appearance models for occlusion handling, in: Proceedings of the 2nd IEEE International Workshop on PETS, Kauai, Hawaii, USA, December 9, 2001.
13. A. Senior, Tracking people with probabilistic appearance models, in: Proceedings of the ECCV Workshop on Performance Evaluation of Tracking and Surveillance Systems, June 1, 2002, pp. 48-55.
14. Wang H., and Suter D. Tracking multiple humans in complex situations. IEEE Int. Conf. Image Process. 2 II (2005) 410-413
15. L.-Q. Xu, P. Puig, A hybrid blob- and appearance-based framework for multi-object tracking through complex occlusions, in: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, October 15-16, 2005, pp. 73-80.
16. Xu M., and Ellis T. Partial observation versus blind tracking through occlusion. Proceedings of the BMVC (2002) 777-786
17. Fleuret F., Lengagne R., and Fua P. Fixed point probability field for complex occlusion handling. 10th IEEE International Conference on Computer Vision vol. 1 (2005) 694-700
18. Pang C.C.C., Lam W.W.L., and Yung N.H.C. A novel method for handling vehicle occlusion in visual traffic surveillance. Image Processing: Algorithms and Systems II, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) vol. 5014 (2003), SPIE-International Society of Optical Engineering, Bellingham 437-447
19. Guha P., Mukerjee A., and Venkatesh K.S. Efficient occlusion handling for multiple agent tracking by reasoning with surveillance event primitives. 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (2005) 49-56
20. T. Yang, Q. Pan, J. Li, S.Z. Li, Real-time multiple objects tracking with occlusion handling in dynamic scenes, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), vol. 1, June 20-25, 2005, pp. 970-975.
21. Conte D., Foggia P., Jolion J.-M., and Vento M. A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions. Pattern Recognition 39 4 (2006) 562-572
22. Jepson A.D., Fleet D.J., and El-Maraghi T.F. Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25 10 (2003) 1296-1311
23. Nguyen H.T., and Smeulders A.W.M. Fast occluded object tracking by a robust appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 268 (2004) 1099-1104
24. Wren C.R., Azarbayejani A., Darrell T., and Pentland A.P. Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19 7 (1997) 780-785
25. Hu M., Hu W.M., and Tan T.N. Tracking people through occlusions. Proceedings of the 17th International Conference on Pattern Recognition vol. 2 (2004), IEEE Computer Society, Los Alamitos 724-727
26. Lumia R., Shapiro L., and Zuniga O. A new connected component algorithm for virtual memory computers. Computer Vision Graphics Image Process. 22 2 (1983) 287-300
27. PETS'2001. 〈http://visualsurveillance.org/PETS2001〉.