Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors

International Journal of Computer Vision

Volumn 75, Issue 2, 2007, Pages 247-266

  Wu, Bo ^a   Nevatia, Ram ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

AdaBoost; Human detection; Human tracking

Indexed keywords

CAMERAS; COMPUTATIONAL METHODS; EDGE DETECTION; FEATURE EXTRACTION; VIDEO STREAMING;

HUMAN DETECTION; HUMAN TRACKING;

TRACKING (POSITION);

EID: 34548102203     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-006-0027-7     Document Type: Article

References (42)

1. Avidan, S. 2005. Ensemble tracking. CVPR, vol. II, pp. 494-501.
2. Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., and Wolf, H.C. 1977. Parametric correspondence and chamfer matching: Two new techniques for image matching. IJCAI, pp. 659-663.
3. Brostow, G.J. and Cipolla, R. 2006. Unsupervised bayesian detection of independent motion in crowds. CVPR, vol. I, pp. 594-601.
4. Comaniciu, D., Ramesh, V. and Meer, P. 2001. The variable bandwidth mean shift and data-driven scale selection. ICCV, vol. I, pp. 438-445.
5. Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. CVPR, vol. I, pp. 886-893.
6. Davis, L., Philomin, V. and Duraiswami, R. 2000. Tracking humans from a moving platform. ICPR, vol. IV, pp. 171-178.
7. Felzenszwalb, P. 2001. Learning models for object recognition. CVPR, vol. I, pp. 56-62.
8. Freund, Y. and Schapire R.E. 1996. Experiments with a New Boosting Algorithm. The 13th Conf. on Machine Learning, pp. 148-156.
9. Gavrila, D. and Philomin, V. 1999. Real-time object detection for "Smart" Vehicles. ICCV, vol. I, pp. 87-93.
10. Gavrila, D. 2000. Pedestrian detection from a moving vehicle. ECCV, vol. II, pp. 37-49.
11. Grabner, H. and Bischof, H. 2006. Online boosting and vision. CVPR, vol. I, pp. 260-267.
12. Huang, C, Ai, H., Wu, B., and Lao. S. 2004. Boosting nested cascade detector for multi-view face detection. ICPR, vol. II, pp. 415-418.
13. Huang, C., Ai, H., Li, Y., and Lao, S. 2005. Vector boosting for rotation invariant multi-view face detection. ICCV, vol. I, pp. 446-453.
14. http://ho.mepages.inf.ed.ac.uk/rbf/CAVIAR/
15. http://www.ic-arda.org/InfoExploit/vace/
16. http://chil.server.de/servlet/is/101/
17. Isard, M. and MacCormick, J. 2001. BraMBLe: A bayesian multipleblob tracker. ICCV, vol. II, pp. 34-41.
18. Kruppa, H., Castrillon-Santana, M., and Schiele, B. 2003. Fast and robust face finding via local context. Joint IEEE Int'l Workshop on VS-PETS.
19. Kuhn, H.W. 1955. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83-87.
20. Lee, M. and Nevada, R. 2006. Human pose tracking using multi-level structured models. ECCV, vol. III, pp. 368-381.
21. Leibe, B., Seemann, E. and Schiele B. 2005. Pedestrian detection in crowded scenes. CVPR, vol. I, pp. 878-885.
22. Lowe, D.G. 1999. Object recognition from local scale-invariant features. ICCV, vol. II, pp. 1150-1157.
23. Mikolajczyk, C., Schmid, C., and Zisserman, A. 2004. Human detection based on a probabilistic assembly of robust part detectors. ECCV, vol. I, pp. 69-82.
24. Mohan, A., Papageorgiou, C., and Poggio, T. 2001. Example-based object detection in. images by components. Trans. PAMI, 23(4):349.
25. Papageorgiou, C., Evgeniou, T., and Poggio, T. 1998. A trainable pedestrian detection system. In Proceeding of Intelligent Vehicles, pp. 241-246.
26. Peter, J.R., Tu, H., and Krahnstoever, N. 2005. Simultaneous estimation of segmentation and shape. CVPR, vol. II, pp. 486-493.
27. Ramanan, D., Forsyth, D.A., and Zisserman, A. 2005. Strike a pose: Tracking people by finding stylized poses. CVPR, vol. I, pp. 271-278.
28. Schapire, R.E. and Singer, Y 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:297-336.
29. Shashua, A., Gdalyahu, Y., and Hayun, G. 2004. Pedestrian detection for driving assistance systems: Single-frame classification and system, level performance. IEEE Intelligent Vehicles Symposium, Parma, Italy, pp. 1-6.
30. Sigal, L., Bhatia, S., Roth, S., Black, M.J., and Isard M. 2004. Tracking loose-limbed people. CVPR, vol. I, pp. 421-428.
31. Smith, K., G.-Perez, D., and Odobez, J.-M. 2005. Using particles to track varying numbers of interacting people. CVPR, vol. I, pp. 962-969.
32. Viola, P. and Jones, M. 2001. Rapid object detection using a boosted cascade of simple features. CVPR, vol. I, pp. 511-518.
33. Viola, P., Jones, M., and Snow, D. 2003. Detecting pedestrians using patterns of motion and appearance. ICCV, pp. 734-741.
34. Wren, C.R., Azarbayejani, A., Darrell, T., and Pentland, A.P. 1997. Pfinder: Real-time tracking of human body. IEEE Trans. PAMI, vol. 19, no. 7.
35. Wu Y., Yu, T., and Hua. G. 2005. A statistical field model for pedestrian detection. CVPR, vol. I, pp. 1023-1030.
36. Wu, B. and Nevatia, R. 2006a. Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet pait detectors. ICCV, vol. I, pp. 90-97.
37. Wu, B. and Nevatia, R. 2006b. Tracking of multiple, partially occluded humans based on static body part detection. CVPR, vol. II, pp. 951-958.
38. Wu, B. and Nevatia, R. 2006c. Tracking of multiple humans in meetings. In V4HCI'06 workshop, in conjunction with CVPR, pp. 143-150.
39. Wu, B., Singh, V.K., Nevatia, R., and Chu, C.-W. (2006). Speaker tssracking in seminars by human body detection. In CLEAR 2006 Evaluation Campaign and Workshop, in conjunction with FG.
40. Zhao, T. and Nevatia, R. 2004a. Tracking multiple humans in crowded environment. CVPR, vol. II, pp. 406-413.
41. Zhao, T. and Nevatia, R. 2004b. Tracking multiple humans in complex situations. IEEE trans. on PAMI, 26(9):1208-1221.
42. Zhao, L. and Davis, L. 2005. Closely coupled object detection and segmentation. ICCV, vol. I, pp. 454-461.