Robust foreground detection with adaptive threshold estimation
复杂背景下的自适应前景分割算法

A robust background subtraction technique is proposed based on adaptive clustering of temporal color/intensity. An un-supervised clustering method is proposed to model a background with a group of weighted clusters. The clusters and their weights can be updated with a background change. In addition, the unimodal or multimodal distributions of background are detected adaptively. We also present a novel statistical threshold estimation scheme to determine the thresholds using in our method. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.