A probabilistic SVM approach for background scene initialization

IEEE International Conference on Image Processing

Volumn 3, Issue , 2002, Pages

  Lin, Horng Horng ^a,b   Liu, Tyng Luh ^a   Chuang, Jen Hui ^b  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECONSTRUCTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; POLYNOMIALS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY;

BACKGROUND SCENE INITIALIZATION; PROBABILISTIC SUPPORT VECTOR MACHINE; VISUAL TRACKING SYSTEMS;

COMPUTER VISION;

EID: 0036453534     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
2. F. De la Torre and M. J. Black, "Robust Principal Component Analysis for Computer vision," Proc. ICCV 2001, vol. 1, pp. 362-369.
3. X. Gao, T.E. Boult, F. Coetzee, and V. Ramesh, "Error Analysis of Background Adaption," Proc. CVPR 2000, vol. 1, pp. 503-510.
4. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, Academic Press, 1981.
5. D. Gutchess, M. Trajkovic, E. Cohen-Solal, D. Lyons, and A. K. Jain, "A Background Model Initialization Algorithm for Video Surveillance," Proc. ICCV 2001, vol. 1, pp. 733-740.
6. I. Haritaoglu, D. Harwood, and L. S. Davis, "A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance," Proc. ICPR 2000, vol. 4, pp. 179-183.
7. N. Jojic and B. J. Frey, "Learning Flexible Sprites in Video Layers," Proc. CVPR 2001, vol. 1, pp. 199-206.
8. Q. Ke and T. Kanade, "A Subspace Approach to Layer Extraction," Proc. CVPR 2001, vol. 1, pp. 255-262.
9. J. T. Kwok, "Moderating the Outputs of Support Vector Machine Classifiers," IEEE Trans Neural Networks, vol. 10, no. 5, Sept., 1999.
10. B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. DARPA IU Workshop, pp. 121-130, 1981.
11. J. C. Platt, "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," Advances in Large-Margin Classifiers, MIT Press, 2000.
12. C. Ridder, O. Munkelt, and H. Kirchner, "Adaptive Background Extimation and Foreground Detection using Kalman-Filtering," Proc. ICAM, pp. 193-199, 1995.
13. J. Rittscher, J. Kato, S. Joga, and A. Blake, "A Probabilistic Background Model for Tracking," Proc. ECCV 2000.
14. C. Stauffer and W. E. L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking," Proc. CVPR 1999, vol. 2, pp. 246-252.
15. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and Practice of Background Maintenance," Proc. ICCV 1999, pp. 255-261.
16. V. Vapnik, "http://www.research.att.com/info/vlad"
17. V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.