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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.

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Index Terms:
Adaptive Gaussian mixture, online EM, background subtraction.
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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