Non-liner learning for mixture of Gaussians

Background modeling plays a key role of event detection in intelligent surveillance systems. Gaussian Mixture Model (GMM) is the wide-used background modeling method in latest surveillance systems. However, the model has some disadvantageous when the object moves slowly. In this paper, we propose a mechanism which takes the advantage of Gaussian error function (ERF) to adjust the growths of each Gaussian's weights and variances, to solve the problem that traditional GMM misjudged the slow moving object as background. The mechanism improves the GMM model to detect the slow moving object accurately and enhance the robustness of surveillance systems.