Social grouping for target handover in multi-view video

Zhen Qin, Christian R. Shelton, Lunshao Chai

Abstract: This paper uses a social grouping model to improve target handover across multiple non-overlapping cameras to enable wide-area video understanding. Prior work focuses on modeling appearance and spatial-temporal cues for target handover. In cameras with different conditions, these cues are weak, at best. We provide a complete generative social grouping model which generalizes a recent single-camera case. Our extension requires strengthening the probabilistic interpretations and the resulting optimization over track handovers and social groupings can be formulated in terms of standard fast algorithms. We demonstrate the effectiveness of the method over existing techniques on challenging real-world multi-camera video.