关键词: 高斯混合模型, 背景模型, 运动分割, 参数估计

Abstract: This paper analyzes the background modeling mechanism using Gaussian mixture models, and then discusses the stability/plasticity dilemma in parameters estimation and update of GMM background model. After that, the importance of Gaussian component’s variance to motion segmentation is pointed out. To solve the slow convergence problem of Gaussian mean and variance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experimental results show that the proposed algorithm improves the accuracy of modal learning and speed of variance convergence.

Key words: Gaussian Mixture Models（GMM）, background model, motion segmentation, parameter estimation