Homography based multiple camera detection and tracking of people in a dense crowd

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Eshel, Ran ^a   Moses, Yael ^a  

^a INTERDISCIPLINARY CENTER   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; PATTERN RECOGNITION;

CHALLENGING PROBLEM; DETECTION AND TRACKING; FIELDS OF VIEW; HEAD DETECTION; HIGH ELEVATIONS; HOMOGRAPHY; ILLUMINATION CONDITIONS; INTENSITY CORRELATIONS; MOTION DIRECTION; OUTDOOR ENVIRONMENTS; SINGLE CAMERAS;

CAMERAS;

EID: 51949089848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587539     Document Type: Conference Paper

References (23)

1. Q. Cai and J.K. Aggarwal. Tracking human motion in structured environments using a distributed-camera system. PAMI, 21(11):1241-1247, 1999.
2. G.K.M. Cheung, T. Kanade, J.Y. Bouguet, and M. Holler. A real time system for robust 3d voxel reconstruction of human motions. In CVPR, pages 714-720, 2000.
3. O.D. Faugeras. Three-Dimensional Computer Vision. MIT Press, Boston MA, 1993.
4. P. F. Felzenszwalb. Learning models for object recognition. In CVPR, pages 56-62, 2001.
5. F. Fleuret, J. Berclaz, R. Lengagne, and P. Fua. Multi-camera people tracking with a probabilistic occupancy map. PAMI, 2007.
6. J. S. Franco and Boyer E. Fusion of multi-view silhouette cues using a space occupancy grid. ICCV, 2:1747-1753, 2005.
7. R. Goldschmidt and Y. Moses. Practical calibration and synchronization in a wide baseline multi-camera setup using blinking LEDs. Technical Report ftp://ftp.idc.ac.il/Pub/Users/cs/yael/TR-2008/IDC-CS-TR-200801, 2008.
8. M. Isard and J. MacCormick. BraMBLe: a Bayesian multiple-blob tracker. In ICCV, pages 34-41, 2001.
9. S.M. Khan and M. Shah. A multiview approach to tracking people in crowded scenes using a planar homography constraint. In ECCV, pages IV: 133-146, 2006.
10. S.M. Khan, P. Yan, and M. Shah. A homographic framework for the fusion of multi-view silhouettes. In ICCV, 2007.
11. J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer. Multi-camera multi-person tracking for easyliving. In International Workshop on Visual Surveillance, 2000.
12. A. Laurentini. The visual hull concept for silhouette-based image understanding. PAMI, 16(2):150-162, 1994.
13. L. Lee, R. Romano, and G. Stein. Monitoring activities from multiple video streams: Establishing a common coordinate frame. PAMI, 22(8):758-767, 2000.
14. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. In CVPR, volume 1, pages 878-885, 2005.
15. A. Mittal and L. Davis. Unified multi-camera detection and tracking using region matching. In Proc. of the IEEE Workshop on Multi-Object Tracking, 2001.
16. C. Papageorgiou and T. Poggio. Trainable pedestrian detection. In IEEE Conference on Intelligent Vehicles, pages 35-39, 1998.
17. M. Quaritsch, M. Kreuzthaler, B. Rinner, Bischof. H., and B. Strobl. Autonomous multicamera tracking on embedded smart cameras. Journal on Embedded Systems, 2007.
18. M. D. Rodriguez and M. Shah. Detecting and segmenting humans in crowded scenes. In In Proc. Intern. Conf. on Multimedia, pages 353-356, 2007.
19. K Smith, D. Gatica-Perez, and J. Odobez. Using particles to track varying numbers of interacting people. In CVPR, pages 962-969, 2005.
20. P. A. Viola, M. J. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. IJCV, 63(2):153-161, 2005.
21. B. Wu and R. Nevatia. Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. IJCV, 75(2):247-266, 2007.
22. Q. Yu, G. Medioni, and I. Cohen. Multiple target tracking using spatio-temporal Markov Chain Monte Carlo data association. In CVPR, 2007.
23. T. Zhao and R. Nevatia. Tracking multiple humans in complex situations. PAMI, 26(9):1208-1221, 2004.