Moving Cast Shadows Detection Using Ratio Edge

Wei Zhang, Xiangzhong Fang, Xiaokang Yang, Q. M. Jonathan Wu

Abstract: Moving objects segmentation plays a very important role in real-time image analysis. However, as one of the common parts in the natural scenes, shadows severely interfere with the accuracy of moving objects detection in video surveillance. In this paper, we present a novel method for moving cast shadows detection. Based on the analysis of the physical model of moving shadows, we prove that the ratio edge is illumination invariant. The distribution of the ratio edge is discussed and a significance test is performed to classify each moving pixel into foreground object or moving shadow. Intensity constraint and geometric heuristics are imposed to further improve the performance. Experiments on various typical scenes exhibit the robustness of the proposed method. Extensively quantitative evaluation and comparison demonstrate that the proposed method significantly outperforms state-of-the-art methods.