Efficient hybrid appearance model for object tracking with occlusion handling

Optical Engineering

Volumn 46, Issue 8, 2007, Pages

  Zhang, Bo ^a   Tian, Weifeng ^a   Jin, Zhihua ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Eigenspace; Hybrid appearance model; Particle filter; Robust statistics

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL MODELS; STATISTICS;

FIXED APPEARANCE MODEL; INTER-MODEL SWITCHING; OBJECT TRACKING;

OBJECT RECOGNITION;

EID: 36049041417     PISSN: 00913286     EISSN: 15602303     Source Type: Journal    
DOI: 10.1117/1.2771656     Document Type: Article

References (18)

1. K. Nummiaro, E. B. Koller-Meier, and L. Van Gool, "An adaptive color-based particle filter," Image Vis. Comput. 21(1), 99-110 (2003).
2. D. Comaniciu, V. Ramesh and P. Meer, "Kernel-based object tracking," IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564-577 (2003).
3. H. T. Nguyen, M. Worring, and R. van den Boomgaard, "Occlusion robust adaptive template tracking," Proc. IEEE Conf. Computer Vision, 1, 678-683 (2001).
4. S. K. Zhou, R. Chellappa, and B. Moghaddam, "Visual tracking and recognition using appearance-adaptive models in particle filters," IEEE Trans. Image Process. 13(11), 1491-1506 (2004).
5. D. Ross, J. W. Lim, and M. H. Yang, "Adaptive probabilistic visual tracking with incremental subspace update," Proc. 8th Euro. Conf. Computer Vision, Vol 2, pp. 470-482 (2004).
6. J. W. Lim, D. Ross, R. S. Lin, and M. H. Yang, "Incremental learning for visual tracking," 18th Ann. Conf. Neural Infor. Process. Syst., pp. 793-800 (2005).
7. A. D. Jepson, D. J. Fleet, and T. F. El-Maraghi, "Robust online appearance models for visual tracking," IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296-1311 (2003).
8. I. Matthews, T. Ishikawa, and S. Baker, "The template update problem," IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 810-815 (2004).
9. H. T. Nguyen and A. W. M. Smeulders, "Fast occluded object tracking by a robust appearance filter," IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1099-1105 (2004).
10. J. L. Tu, H. Tao, and T. S. Huang, "Online updating appearance generative mixture model for meanshift tracking," Proc. 7th Asian Conf. Computer Vision., pp. 694-703 (2006).
11. S. G. Torre and S. McKenna, "View-based adaptive affine tracking," Proc. 5th Euro. Conf. Computer Vision, pp. 828-842 (1998).
12. M. J. Black, D. J. Fleet, and Y. Yacoob, "A framework for modeling appearance change in image sequences," Proc. IEEE Conf. Computer Vision, pp. 660-667 (1998).
13. A. P. Li, Z. L. Jin, and S. Q. Hu, "Learning-based appearance model for probabilistic visual tracking." Opt. Eng. 45, 077204 (2006).
14. D. Skočaj, "Robust subspace approaches to visual learning and recognition," PhD Thesis, Univ. Ljubljana, Slovenia (2003).
15. M. Isard and A. Blake, "CONDENSATION-conditional density propagation for visual tracking," Int. J. Comput. Vis. 29(1), 5-28 (1998).
16. R. Van der Merwe, A. Doucet, N. de Freitas, and E. Wan, "The unscented particle filter," Tech. Report CUED/F-INFENG/ TR380, Cambridge Univ., Department of Engineering, UK (2000).
17. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE Proc. F, Radar Signal Process. 140(2), 107-113 (1993).
18. http://www.cs.toronto.edu/~dross/ivt/; http://www.ces.clemson.edu/~stb/ research/headtracker/; and http://groups.inf.ed.ac.uk/vision/CAVIAR/ CAVIARDATA1/.