Learning-based appearance model for probabilistic visual tracking

Optical Engineering

Volumn 45, Issue 7, 2006, Pages

  Li, Anping ^a   Jing, Zhongliang ^a   Hu, Shiqiang ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Adaptive appearance model; EM algorithm; Gaussian mixture model; Particle filter; Robust statistics technique

Indexed keywords

ADAPTIVE ALGORITHMS; IMAGE QUALITY; OPTIMIZATION; STATISTICAL METHODS;

ADAPTIVE APPEARANCE MODEL; EXPECTATION MAXIMIZATION ALGORITHM; GAUSSIAN MIXTURE MODEL; PARTICLE FILTER; ROBUST-STATISTICS TECHNIQUE;

IMAGING TECHNIQUES;

EID: 33749407370     PISSN: 00913286     EISSN: 15602303     Source Type: Journal    
DOI: 10.1117/1.2227276     Document Type: Article

References (15)

1. D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2, pp. 142-149 (2000).
2. T L. Liu and H. T. Chen, "Real-time tracking using trust-region methods," IEEE Trans. Pattern Anal. Mach. Intell 26(3), 397-402 (2004).
3. G. D. Hager and P. N. Belhumeur, "Efficient region tracking with parametric models of geometry and illumination," IEEE Trans. Pattern Anal. Mach Intell. 20(10), 1025-1039 (1998).
4. N. Papanikolopoulos, P. Khosla, and T. Kanade, "Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision," IEEE Trans. Rob. Autom. 9(1), 14-35 (1993).
5. H. Sidenbladh, M. J. Black, and D. J. Fleet, "Stochastic tracking of 3D human figures using 2D image motion," in Proc. Sixth European Conf. on Computer Vision, Vol. 2, pp. 702-718, European Vision Soc. (2000).
6. Y. Wu and T S. Huang, "Color tracking by transductive learning," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, pp. 133-138 (2000).
7. M. J. Black and A. D. Jepson, "Eigentracking: robust matching and tracking of articulated objects using view-based representation," Int J. Comput. Vis. 26(1), 63-94 (1998).
8. D. Ross, J. Lim, and M.-H. Yang, "Adaptive probabilistic visual tracking with incremental subspace update," in Proc. Eighth European Conf. on Computer Wsion, Vol. 2, pp. 470-482, European Vision Soc. (2004).
9. 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).
10. Y. Wu and T. S. Huang, "A co-inference approach for robust visual tracking," in Proc. IEEE Int. Conf. on Computer Vision, Vol. 2, pp. 26-33 (2001).
11. 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).
12. D-S. Lee, "Effective Gaussian mixture learning for video background subtraction," IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827-832 (2005).
13. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. Ser. B. Methodol. 39, 1-38 (1977).
14. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process. 50(2), 174-188 (2002).
15. Some test sequences are available at the Web sites http:// vision.stanford.edu/~birch, http://www.cs.toronto.edu/~doss/ivt, and http://www.cs.toronto.edu/vis/projects/dudekfaceSequence.html