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Effective Gaussian Mixture Learning for Video Background Subtraction
May 2005 (vol. 27 no. 5)
pp. 827-832
Adaptive Gaussian mixtures have been used for modeling nonstationary temporal distributions of pixels in video surveillance applications. However, a common problem for this approach is balancing between model convergence speed and stability. This paper proposes an effective scheme to improve the convergence rate without compromising model stability. This is achieved by replacing the global, static retention factor with an adaptive learning rate calculated for each Gaussian at every frame. Significant improvements are shown on both synthetic and real video data. Incorporating this algorithm into a statistical framework for background subtraction leads to an improved segmentation performance compared to a standard method.

[1] A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum Likelihood from Incomplete Data Via the EM Algorithm,” J. Royal Statistical Soc. B, vol. 39, pp. 1-38, 1977.
[2] N. Friedman and S. Russell, “Image Segmentation in Video Sequences: A Probabilistic Approach,” Proc. 13th Conf. Uncertainty in Artificial Intelligence, Aug. 1997.
[3] M. Harville, G. Gordon, and J. Woodfill, “Foreground Segmentation Using Adaptive Mixture Models in Color and Depth,” Proc. ICCV Workshop Detection and Recognition of Events in Video, July 2001.
[4] P. KaewTraKulPong and R. Bowden, “An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection,” Proc. European Workshop Advanced Video Based Surveillance Systems, Sept. 2001.
[5] S.J. McKenna, Y. Raja, and S. Gong, “Object Tracking Using Adaptive Color Mixture Models,” Proc. Asian Conf. Computer Vision, vol. 1, pp. 615-622, Jan. 1998.
[6] R.M. Neal and G.E. Hinton, “A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants,” Learning in Graphical Models, 1998.
[7] S.J. Nowlan, “Soft Competitive Adaptation: Neural Network Learning Algorithms Based on Fitting Statistical Mixtures,” PhD thesis, Carnegie Mellon Univ., 1991.
[8] M.A. Sato and S. Ishii, “Online EM Algorithm for the Normalized Gaussian Network,” Neural Computation, vol. 12, pp. 407-432, 1999.
[9] C. Stauffer and W.E.L. Grimson, “Adaptive Background Mixture Models for Real-Time Tracking,” Proc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, June 1999.
[10] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: Principles and Practice of Background Maintenance,” Proc. Int'l Conf. Computer Vision, pp. 255-261, Sept. 1999.

Index Terms:
Adaptive Gaussian mixture, online EM, background subtraction.
Citation:
Dar-Shyang Lee, "Effective Gaussian Mixture Learning for Video Background Subtraction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827-832, May 2005, doi:10.1109/TPAMI.2005.102
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