Crowd Segmentation Using both Appearance and Stereo Information

Crowd segmentation is an important issue in video surveillance. With the decrease in their cost, stereo cameras can be used to help develop new algorithms to achieve better accuracy in crowd segmentation. This paper aims to develop a method to explore the depth cues for crowd segmentation in video surveillance. The contributions of this paper are twofold. First, a novel crowd segmentation method closely coupling appearance and stereo information has been developed. Instead of performing disparity calculation as a preprocessing step, stereo information is obtained concurrently with appearance-based crowd segmentation. Second, an object-level disparity algorithm is proposed for object segmentation in surveillance scenarios. Only one disparity value for each hypothetical object greatly reduces the computational complexity and simplifies the segmentation method. Experimental results and quantitative evaluations based on two surveillance scenarios are presented in this paper. The results consistently show the effectiveness of the algorithm in exploring depth cues for crowd segmentation.