Density-based multifeature background subtraction with support vector machine

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 34, Issue 5, 2012, Pages 1017-1023

  Han, Bohyung ^a   Davis, Larry S ^b  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b Institute of Advanced Computer Science   (United States)

Author keywords

Background modeling and subtraction; Haar like features; kernel density approximation; support vector machine

Indexed keywords

BACKGROUND MODEL; BACKGROUND MODELING; BACKGROUND SUBTRACTION; BINARY SEGMENTATION; DENSITY-BASED; DENSITY-BASED METHOD; FEATURE COMBINATION; HAAR-LIKE FEATURES; HIGH-LEVEL COMPUTER VISION; ILLUMINATION CHANGES; KERNEL DENSITY; MODELING TECHNIQUE; MULTIPLE FEATURES; OBJECT DETECTION; PRE-PROCESSING STEP; SPATIAL VARIATIONS; SPATIO-TEMPORAL VARIATION; STATIC CAMERAS; SUPPORT VECTOR MACHINE (SVM);

ALGORITHMS; COMPUTER VISION; MODELS;

SUPPORT VECTOR MACHINES;

EID: 84859175807     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.243     Document Type: Article

References (32)

1. A. Elgammal, D. Harwood, and L. Davis, "Non-Parametric Model for Background Subtraction," Proc. European Conf. Computer Vision, pp. 751-767, June 2000.
2. C. Stauffer and W.E.L. Grimson, "Learning Patterns of Activity Using Real-Time Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug. 2000.
3. B. Han, D. Comaniciu, and L. Davis, "Sequential Kernel Density Approximation through Mode Propagation: Applications to Background Modeling," Proc. Asian Conf. Computer Vision, 2004.
4. D.S. Lee, "Effective Gaussian Mixture Learning for Video Background Subtraction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827-832, May 2005.
5. Z. Zivkovic and F. van der Heijden, "Efficient Adaptive Density Estimation Per Image Pixel for Task of Background Subtraction," Pattern Recognition Letters, vol. 27, no. 7, pp. 773-780, 2006. (Pubitemid 43374860)
6. P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
7. B. Han, D. Comaniciu, Y. Zhu, and L.S. Davis, "Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1186-1197, July 2008.
8. B. Han and L.S. Davis, "Adaptive Background Modeling and Subtraction: A Density-Based Approach with Multiple Features" Intelligent Video Surveillance Systems and Technology, Y. Ma and G. Qian eds., ch. 4, pp. 79-103, CRC Press, 2010.
9. I. Haritaoglu, D. Harwood, and L.S. Davis, "W4: Real-Time Surveillance of People and Their Activities," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug. 2000.
10. K. Kim, T.H. Chalidabhongse, D. Harwood, and L. Davis, "Real-Time Foreground-Background Segmentation Using Codebook Model," Real-Time Imaging, vol. 11, no. 3, pp. 172-185, 2005. (Pubitemid 40929458)
11. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-Time Tracking of the Human Body," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, July 1997. (Pubitemid 127762271)
12. N. Friedman and S. Russell, "Image Segmenation in Video Sequences: A Probabilistic Approach," Proc. 13th Conf. Uncertainty in Artificial Intelligence, 1997.
13. A. Mittal and D. Huttenlocher, "Scene Modeling for Wide Area Surveillance and Image Synthesis," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
14. R.M. Neal and G.E. Hinton, "A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants," Learning in Graphical Models, M.I. Jordan, ed., pp. 355-368, Kluwer Academic, 1998.
15. A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. I, pp. 415-422, 2001.
16. C.E. Priebe and D.J. Marchette, "Adaptive Mixture Density Estimation," Pattern Recognition, vol. 26, no. 5, pp. 771-785, 1993.
17. S.J. McKenna, Y. Raja, and S. Gong, "Tracking Colour Objects Using Adaptive Mixture Models," Image and Vision Computing J., vol. 17, pp. 223-229, 1999.
18. A. Mittal and N. Paragios, "Motion-based Background Subtraction Using Adaptive Kernel Density Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
19. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and Practice of Background Maintenance," Proc. Seventh Int'l Conf. Computer Vision, pp. 255-261, 1999. (Pubitemid 32080048)
20. O. Javed and M. Shah, "Tracking and Object Classification for Automated Surveillance," Proc. European Conf. Computer Vision, pp. 343-357, 2002.
21. N. Paragios and V. Ramesh, "A MRF-Based Approach for Real-Time Subway Monitoring," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. I, pp. 1034-1040, 2001.
22. M. Seki, T. Wada, H. Fujiwara, and K. Sumi, "Background Subtraction Based on Coocurence of Image Variations," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.
23. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, "Background Modeling and Subtraction of Dynamic Scenes," Proc. Ninth IEEE Int'l Conf. Computer Vision, 2003.
24. J. Zhong and S. Sclaroff, "Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 44-50, 2003.
25. M. Heikkilä and M. Pietikäinen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657-662, Apr. 2006. (Pubitemid 43264663)
26. L. Li, W. Huang, I. Gu, and Q. Tian, "Statistical Modeling of Complex Backgrounds for Foreground Object Detection," IEEE Trans. Image Processing, vol. 13, no. 11, pp. 1459-1472, Nov. 2004.
27. T. Parag, A. Elgammal, and A. Mittal, "A Framework for Feature Selection for Background Subtraction," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1916-1923, 2006. (Pubitemid 44931550)
28. R.E. Schapire, "The Strength of Weak Learnability," Machine Learning, vol. 5, no. 2, pp. 197-227, 1990.
29. H.-H. Lin, T.-L. Liu, and J.-H. Chuang, "Learning a Scene Background Model via Classification," IEEE Trans. Signal Processing, vol. 57, no. 5, pp. 1641-1654, May 2009.
30. Z. Hao, W. Wen, Z. Liu, and X. Yang, "Real-Time Foreground- Background Segmentation Using Adaptive Support Vector Machine Algorithm," Proc. 17th Int'l Conf. Artificial Neural Networks, pp. 603-610, 2007.
31. J. Zhang and C.H. Chen, "Moving Objects Detection and Segmentation in Dynamic Video Backgrounds," Proc. IEEE Conf. Technologies for Homeland Security, pp. 64-69, 2007. (Pubitemid 47432407)
32. "CAVIAR: Context Aware Vision using Image-Based Active Recognition," http://homepages.inf.ed.ac.uk/rbf/CAVIAR/, 2012.