The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2012 vol.34)
pp: 1017-1023
Bohyung Han , POSTECH (Pohang University of Science and Technology), Pohang
Larry S. Davis , University of Maryland, College Park
ABSTRACT
Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.
INDEX TERMS
Background modeling and subtraction, Haar-like features, support vector machine, kernel density approximation.
CITATION
Bohyung Han, Larry S. Davis, "Density-Based Multifeature Background Subtraction with Support Vector Machine", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 5, pp. 1017-1023, May 2012, doi:10.1109/TPAMI.2011.243
REFERENCES
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[32] "CAVIAR: Context Aware Vision using Image-Based Active Recognition," http://homepages.inf.ed.ac.uk/rbfCAVIAR/, 2012.
37 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool