Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

Journal of Electronic Imaging

Volumn 16, Issue 2, 2007, Pages

  Zhang, Wei ^a   Fang, Xiangzhong ^a   Yang, Xiaokang ^a   Wu, Q M Jonathan ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY OF WINDSOR   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX MOTION; FLAGS FLUTTERING; GAUSSIAN MIXTURE MODEL; SPATIOTEMPORAL GAUSSIAN MIXTURE MODEL;

IMAGE QUALITY; IMAGE RESOLUTION; PIXELS;

IMAGE SEGMENTATION;

EID: 34548069655     PISSN: 10179909     EISSN: 1560229X     Source Type: Journal    
DOI: 10.1117/1.2731329     Document Type: Article

References (22)

1. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, "Background modeling and subtraction of dynamic scenes," in Int. Conf. on Computer Vision, pp. 1305-1312 (2003).
2. S. Barotti, L. Lombardi, and P. Lombardi, "Multi-module switching and fusion for robust video surveillance," in Int. Conf. on Image Analysis and Processing, pp. 260-265 (2003).
3. N. Ohta, "A statistical approach to background subtraction for surveillance systems," in Int. Conf. on Computer Vision, Vol. 2, pp. 481-486 (2001).
4. Q. Zhang and R. Klette, "Robust background subtraction and maintenance," in Int. Conf. on Pattern Recognition, Vol. 2, pp. 90-93 (2004).
5. P. Shi, E. G. Jones, and Q. Zhu, "Median model for background subtraction in intelligent transportation system," in Image Processing: Algorithms and Systems III, Proc. SPIE 5298, 168-176 (2004).
6. L. D. Stefano, S. Mattoccia, and M. Mola, "A change-detection algorithm based on structure and color," in Proc. IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, pp. 252-259 (2003).
7. M. Seki, T. Wada, H. Fujiwara, and K. Sumi, "Background subtraction based on cooccurrence of image variations," in IEEE Conf. Comput. Vis. Pattern Recogn., Vol. 2, pp. II/65-II/72 (2003).
8. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance," Proc. IEEE 90, 1151-1163 (2002).
9. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and practice of background maintenance," in Int. Conf. Comput. Vis., Vol. 1, pp. 255-261 (1999).
10. K.-W. Lee and J. Kim, "Moving object segmentation based on statistical motion model," Electron. Lett. 35, 1719-1720 (1999).
11. L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, "Statistical modeling of complex backgrounds for foreground object detection," IEEE Trans. Image Process. 13, 1459-1472 (2004).
12. W. Zhang, X. Z. Fang, and X. K. Yang, "Moving vehicles segmentation based on Bayesian framework for Gaussian motion model," Pattern Recogn. Lett. 27, 956-967 (2006).
13. D. Toth and T. Aach, "Detection and recognition of moving objects using statistical motion detection and Fourier descriptors," in Int. Conf. on Image Analysis and Processing, pp. 430-435 (2003).
14. N. Frieman, and S. Russell, "Image segmentation in video sequences: a probabilistic approach," in Proc. Conf. Uncertainty in Artificial Intelligence, pp. 175-181 (1997).
15. B. Stenger, V. Ramesh, N. Paragios, F. Coetzee, and J. M. Buhmann, "Topology free hidden Markov models: application to background modeling," in Int. Conf. on Computer Vision, Vol. 1, pp. 94-301 (2001).
16. C. Stauffer and W. E. L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000).
17. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Int. Conf. on Systems, Man, and Cybernetics, Vol. 2, pp. 28-31 (2004).
18. D. Zhou and H. Zhang, "Modified GMM background modeling and optical flow for detection of moving objects," in Int. Conf. on Systems, Man, and Cybernetics, Vol. 3, pp. 2224-2229 (2005).
19. Y. Sun and B. Yuan, "Heirarchical GMM to handle sharp changes in moving object detection," Electron. Lett. 40, 801-802 (2004).
20. K. T. P. Pakorn and B. Richard, "A real time adaptive visual surveillance system for tracking low-resolution color targets in dynamically changing scenes," Image Vis. Comput. 21, 913-929 (2003).
21. M. Harville, "A framework for high-level feedback to adaptive, perpixel, mixture-of-Gaussian background models," in European Conf. Computer Vision, Vol. 3, pp. 543-560 (2002).
22. R. Pless, J. Larson, S. Siebers, and B. Westover, "Evaluation of local models of dynamic backgrounds," in IEEE Conf. Comput. Vis. Pattern Recogn, Vol. 2, pp. II/73-II/78 (2003).