Flexible background mixture models for foreground segmentation

Image and Vision Computing

Volumn 24, Issue 5, 2006, Pages 473-482

  Cheng, Jian ^a   Yang, Jie ^a   Zhou, Yue ^a   Cui, Yingying ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Background subtraction; EM algorithm; Foreground segmentation; Maximum a posteriori (MAP); Mixture models

Indexed keywords

APPROXIMATION THEORY; ASYMPTOTIC STABILITY; CAMERAS; COMPUTER APPLICATIONS; COMPUTER VISION; LEARNING ALGORITHMS; MATHEMATICAL MODELS; STOCHASTIC CONTROL SYSTEMS;

BACKGROUND SUBTRACTION; EM ALGORITHM; FOREGROUND SEGMENTATION; MAXIMUM A POSTERIORI (MAP); MIXTURE MODELS;

IMAGE SEGMENTATION;

EID: 33646855413     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2006.01.018     Document Type: Article

References (23)

1. I. Haritaoglu, D. Harwood, L.S. Davis, W4: Who? When? Where? What? A real-time system for detecting and tracking people, in: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 222-227.
2. W.E. Grimson, C. Stauffer, R. Romano, L. Lee, Using adaptive tracking to classify and monitor activities in a site, in: Proceedings of the IEEE Computer Vision and Pattern Recognition, 1998, pp. 22-29.
3. Stauffer C., and Grimson W.E. Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Vision and Pattern Recognition 2 (1999) 246-252
4. N. Friedman, S. Russell, Image segmentation in video sequences: a probabilistic approach, in: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997, pp. 175-181.
5. Kato J., Watanabe T., Joga S., Rittscher J., and Blake A. An HMM-based segmentation method for traffic monitoring movies. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 9 (2002) 1291-1296
6. Wren C.R., Azarbayejani A., Darrell T., and Pentland A.P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 7 (1997) 780-785
7. C. Ridder, O. Munkelt, H. Kirchner, Adaptive background estimation and foreground detection using kalman-filtering, in: Proceedings of the International Conference on recent Advances in Mechatronics, 1995, pp. 193-199.
8. J. Rittscher, J. Kato, S. Joga, A. Blake, A probabilistic background model for tracking, in: Proceedings of the Sixth European Conference on Computer Vision, 2000, pp. 336-350.
9. Stenger B., Ramesh V., Paragios N., Coetzee F., and Buhmann J.M. Topology free hidden markov models: application to background modeling. Proceedings of the IEEE International Conference on Computer Vision 1 (2001) 294-301
10. P.W. Power, J.A. Schoonees, Understanding background mixture models for foreground segmentation, in: Proceedings of the Image and Vision Computing New Zealand, 2002, pp. 267-271.
11. Toyama K., Krumm J., Brumitt B., and Meyers B. Wallflower: principles and practice of background maintenance. Proceedings of the IEEE International Conference on Computer Vision 1 (1999) 255-261
12. McLachlan G., and Peel D. Finite Mixture Models (2000), Wiley, London
13. Webb A.R. Statistical Pattern Recognition. second ed. (2002), Wiley, London
14. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39 1 (1977) 1-38
15. Figueiredo M.A.T., and Jain A.K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 3 (2002) 381-396
16. Zivkovic Z., and van der Heijden F. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 5 (2004) 651-656
17. Brand M.E. Structure learning in conditional probability models via an entropic prior and parameter extinction. Neural Computation 11 5 (1999) 1155-1182
18. Rasmussen C. The infinite gaussian mixture model. Advances in Neural Information Processing Systems 12 (2000) 554-560
19. Richardson S., and Green P. On bayesian analysis of mixture models with unknown number of components. Journal of the Royal Statistical Society, Series B 59 4 (1997) 731-792
20. Wallace C., and Dowe D. Minimum message length and Kolmogorov complexity. Computer Journal 42 4 (1999) 270-283
21. Ziou D., and Bouguila N. Unsupervised learning of a finite gamma mixture using MML: application to SAR image analysis. Proceedings of theIEEE 17th International Conference on Pattern Recognition 2 (2004) 68-71
22. Rissansen J. Stochastic complexity. Journal of the Royal Statistical Society, Series B 49 3 (1987) 223-239
23. R. Cucchiara, C. Grana, M. Piccardi, A. Prati, Detecting objects, shadows and ghosts in video streams by exploiting color and motion information, in: Proceedings of the IEEE 11th International Conference on Image Analysis and Processing, 2001, pp. 360-365.