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Improving Clustering-Based Background Modeling Techniques Using Markov Random Fields  

Hahn, Hee-Il (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Park, Soo-Bin (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.1, 2011 , pp. 157-165 More about this Journal
Abstract
It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.
Keywords
Visual surveillance; Background subtraction; Pixel-based background modeling; Markov random fields;
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1 L. Zhang and S. M. Seitz, "Estimating optimal parameters for MRF stereo from a single image pair," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.2, pp.331-342, Feb. 2007.   DOI
2 J. M. McHugh, J. Konrad, V. Saligrama, and P. Jodoin, "Foreground-adaptive background subtraction," IEEE Signal Processing Letters, vol.16, issue 5, pp.390-393, 2009.   DOI
3 K. Kim, T.H. Chalidabhongse, D. Harwood, "Real-time foreground-background segmentation using codebook model," Elsevier Real-Time Imaging, vol. 11, pp. 172-185, 2005.   DOI   ScienceOn
4 J. Migdal and W. E. Grimson, "Background subtraction using Markov thresholds," Proceedings of the IEEE Workshop on Motion and Video Computing(WACV/MOTION'05), 2005.
5 M. Wu and X. Peng, "Spatio-temporal context for codebook-based dynamic background subtraction," International Journal of Electronics and Communications, pp. 739-747, 2010.
6 M. Berthod, Z. Kato, S. Yu, J. Zerubia, "Bayesian image classification using Markov random fields," Image Vision Comput 1996; 14:285-95.   DOI   ScienceOn
7 Y. Chen, C. Chen, C. Huang, Y. Hung, "Efficient hierarchical method for background subtraction," Pattern Recognition, pp. 2706-2715, 2007.
8 J. S. Yedidia, W. T. Freeman and Y. Weiss, "Understanding belief propagation and its generalizations," TR-2001-22, Jan. 2002.
9 P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient belief propagation for early vision," Proc. Conf. Computer Vision and Pattern Reccognition, pp.261-268, 2004.
10 C. Stauffer, W.E.L. Grimson, "Adaptive background mixture models for real-time tracking,' international Journal of Computer Vision, IEEE International Conference on Computer Vision and Pattern Recognition, Vol. 2, pp.246-252, 1999.
11 D. Buttler, S. Sridharan, V.M. Bove Jr., "Real-time adaptive background segmentation,." ICASSP'03, pp. 349-352, 2003.
12 A. Elgammal, R. Duraiswami, D. harwood, L.S. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance," pp. 1151-1163, Proc. IEEE, vol. 90, no. 7, Jul, 2002.   DOI   ScienceOn