Research on GMM Background Modeling and its Covariance Estimation

Abstract:

This paper analyzes the background modeling mechanism using Gaussian mixture model and the stability /plasticity dilemma in parameters estimation of GMM background model. To solve the slow convergence problem of Gaussian mean and covariance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experiments show that the proposed algorithm improves the accuracy of modal learning and speed of covariance convergence.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 2327-2333
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.2327
• Online since: November 2011
• Authors: Yun Chu Zhang, Ru Min Zhang, Shi Jun Song
• Keywords: Background Modeling, Gaussian Mixture Model, Motion Segmentation, Parameter Estimation

References

[1] Douglas A. Reynolds, Thomas F. Quatieri, Robert B. Dunn, Speaker verification using adapted Gaussian mixture models, Digital Signal Processing, vol. 10, (1-3). 2000, pp.19-41.

DOI: 10.1006/dspr.1999.0361

[2] Friedman N, Russell S, Image segmentation in video sequences: A probabilistic approach, Proc. of the 13th Conference on Uncertainty in Artificial Intelligence. San Francisco, 1997, pp.175-181.

[3] Stauffer C, Grimson W, Adaptive background mixture models for realtime tracking, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, USA, 1999, pp.246-252.

DOI: 10.1109/cvpr.1999.784637

[4] Bouwmans, F EI Baf, B Vachon, Background modeling using mixture of gaussians for foreground detection-a survey, Recent Patents on Computer Science, vol. 1(3), 2008, pp.219-237.

DOI: 10.2174/2213275910801030219

[5] B Awwad Shiekh Hasan, J.Q. Gan, Sequential EM for unsupervised adaptive Gaussian mixture model based classifier, Proc. of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, 2009, pp.96-106.

DOI: 10.1007/978-3-642-03070-3_8

[6] Wang Weibin, Zhong Yuntian, A clustering algorithm based on greedy EM algorithm learning GMM, Computer Simulation, vol. 24(2), 2007, pp.65-68.

[7] Christopher M Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006: 423-455.

[8] Allou Samé, Christophe Ambroise, Gérard Govaert, An online classification EM algorithm based on the mixture model, Statistics and Computing, vol. 17(3), 2007, p.209–218.

DOI: 10.1007/s11222-007-9017-z

[9] Richard O Duda, Peter E Hart, David G Stork, Pattern classification. (2nd Edition) New York: John Wiley, 2000: 517-580.

[10] Yu Peng, Tong Xingwei, Feng Jufu, Unsupervised classification based on penalized Maximum Likelihood of Gaussian mixture models, Chinese Journal of Applied Probability and Statistics, vol. 24(5), 2008, pp.475-483.