Variational particle filter for multi-object tracking

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Jin, Yonggang ^a   Mokhtarian, Farzin ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; CLUTTER (INFORMATION THEORY); COMPUTER NETWORKS; COMPUTER VISION; IMAGE PROCESSING; MIXTURES; NONLINEAR FILTERING; PARAMETER ESTIMATION; SIGNAL FILTERING AND PREDICTION; WAVE FILTERS;

PARTICLE FILTERING;

TARGET TRACKING;

EID: 50649112325     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4408952     Document Type: Conference Paper

References (21)

1. Y. Bar-Shalom and T. Fortmann. Tracking and Data Association. Academic Press, 1988.
2. A. Baumberg and D. Hogg. Learning flexible models from image sequences. In Proc. ECCV, pages 299-308, 1994.
3. A. Blake, R. Curwen, and A. Zisserman. A framework for spatio-temporal control in the tracking of visual contours. IJCV, 11(2):127-145, 1993.
4. A. Blake and M. Isard. Active Contours. Springer, 1998.
5. J. Canny. A computational approach to edge detection. IEEE PAMI, 8(6):679-698, Nov. 1986.
6. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Proc. IEEE CVPR, pages 142-149, 2000.
7. A. Elgammal and L. Davis. Probabilistic framework for segmenting people under occlusion. In Proc. IEEE ICCV, pages 145-152, 2001.
8. A. Elgammal, R. Duraiswami, and L. Davis. Probabilistic tracking in joint feature-spatial spaces. In Proc. IEEE CVPR, pages 781-788, 2003.
9. M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In Proc. ECCV, pages 343-356, 1996.
10. M. Isard and J. MacCormick. BraMBLe: a Bayesian multiple-blob tracker. In Proc. IEEE ICCV, pages 34-41, 2001.
11. A. D. Jepson, D. J. Fleet, and T. F. Ei-Maraghi. Robust online appearance models for visual tracking. IEEE PAMI, 25(10):1296-1311, 2003.
12. Z. Khan, T. Balch, and F. Dellaert. An MCMC-based particle filter for tracking multiple interacting targets. In Proc. ECCV, pages 279-290, 2004.
13. J. MacCormick and A. Blake. A probabilistic exclusion principle for tracking multiple objects. In Proc. IEEE ICCV, pages 572-578, 1999.
14. K. Okuma, A. Taleghani, N. de Freitas, J. J. Little, and D. G. Lowe. A boosted particle filter: Multitarget detection and tracking. In Proc. ECCV, pages 28-39, 2004.
15. A. Pece and A. Worrall. Tracking with the EM contour algorithm. In Proc. ECCV, pages 3-17, 2002.
16. R. Streit and T. Luginbuhl. Maximum likelihood method for probabilistic multi-hypothesis tracking. Proc. SPIE, 2235:394-405, 1994.
17. J. Sullivan, A. Blake, M. Isard, and J. MacCormick. Object localization by Bayesian correlation. In Proc. IEEE ICCV, pages 1068-1075, 1999.
18. J. Vermaak, A. Doucet, and P. Pérez. Maintaining multimodality through mixture tracking. In Proc. IEEE ICCV, pages 1110- 1116, 2003.
19. J. Vermaak, N. Lawrence, and P. Pérez. Variational inference for visual tracking. In Proc. IEEE CVPR, pages 773-780, 2003.
20. Y. Wu, T. Yu, and G. Hua. Tracking appearances with occlusions. In Proc. IEEE CVPR, pages 789-795, 2003.
21. C. Yang, R. Duraiswami, N. Gumerov, and L. Davis. Improved fast Gauss transform and efficient kernel density estimation. In Proc. IEEE ICCV, pages 464-471, 2003.