Robust estimation for background subtraction

First International Conference on Innovative Computing, Information and Control 2006, ICICIC'06

Volumn , Issue , 2006, Pages 660-663

  Zhang, Hongxun ^a   Xu, De ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; MICROFLUIDICS; MODAL ANALYSIS; OBJECT RECOGNITION;

BACKGROUND MAINTENANCE; BACKGROUND OBJECTS; BACKGROUND SUBTRACTION; BREAKDOWN (BD); COMPLEX ENVIRONMENTS; IMAGE SEQUENCING; INTERNATIONAL CONFERENCES; MOVING REGIONS (MR); MOVING TARGETS; ROBUST ESTIMATIONS; ROBUST ESTIMATORS; SENSITIVE DETECTION; TREE BRANCHES;

ESTIMATION;

EID: 45849090386     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICIC.2006.150     Document Type: Conference Paper

References (16)

1. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance", Proceedings of the IEEE, vol. 90, 2002, pp. 1151-1163.
2. N. Friedman and S. Russell, "Image segmentation in video sequences: A probabilistic approach", In 13th Conference on Uncertainty in Artificial Intelligence, 1997, pp. 175-181.
3. W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee, "Using adaptive tracking to classify and monitor activities in a site", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, pp. 22-29.
4. S. Jabri, Z. Duric, H. Wechsler, and A. Rosenfeld, "Detection and location of people in video images using adaptive fusion of color and edge information", In 15th International Conference on Pattern Recognition, vol. 4, 2000, pp. 627-630.
5. K.P. Karmann and A. Brandt, "Moving object recognition using an adaptive background memory", In Cappellini V. (ed.) Time-Varying Image Processing and Moving Object Recognition, vol. 2, 1990.
6. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, "Towards robust automatic traffic scene analysis in real-time", In 12th IAPR International Conference on Pattern Recognition, vol. 1, 1994, pp. 126-131.
7. M. Mason and Z. Duric, "Using histograms to detect and track objects in color video", In 30th Applied Imagery Pattern Recognition Workshop, 2001, pp. 154-159.
8. T. Matsuyama, T. Ohya, and H. Habe, "Background subtraction for non-stationary scenes", In 4th Asian Conference on Computer Vision, 2000, pp. 622-667.
9. C. Ridder, O. Munkelt, and H. Kirchner, "Adaptive background estimation and foreground detection using Kalman-filtering", In International Conference on Recent Advances in Mechatronics, 1995, pp. 193-199.
10. J. Rittscher, J. Kato, S. Joga, and A. Blake, "A probabilistic background model for tracking", In 6th European Conference on Computer Vision, vol. 2, 2000, pp. 336-350.
11. C. Stauffer and W.E.L. Grimson, "Adaptive background mixture models for real-time tracking", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 1999, pp. 246-252.
12. B. Stenger, V. Ramesh, N. Paragios, F. Coetzee, and J.M. Buhmann, "Topology free hidden markov models: Application to background modeling", In 8th IEEE International Conference on Computer Vision, vol. 1, 2001, pp. 294-301.
13. L. Wixon, "Detecting salient motion by accumulating directionally-consistent flow", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, 2000, pp. 774-780.
14. C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, "Pfinder: Real-time tracking of the human body", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, 1997, pp. 780-785.
15. J. Zhong and S. Sclaroff, "Segmenting foreground objects from a dynamic textured background via a robust Kaiman filter", In 9th IEEE International Conference on Computer Vision, vol. 1, 2003, pp. 44-50.
16. Peter J. Rousseeuw and Sabine Verboven, "Robust estimation in very small samples", Computational Statistics & Data Analysis, vol. 40, 2002, pp. 741-758.