Group Structure Preserving Pedestrian Tracking in a Multicamera Video Network
Pedestrian tracking in video has been a popular research topic with many practical applications. In order to improve tracking performance, many ideas have been proposed, among which the use of geometric information is one of the most popular directions in recent research. In this paper, we propose a novel multicamera pedestrian tracking framework, which incorporates the structural information of pedestrian groups in the crowd. In this framework, first, a new cross-camera model is proposed, which enables the fusion of the confidence information from all camera views. Second, the group structures on the ground plane provide extra constraints between pedestrians. Third, the structured support vector machine is adopted to update the cross-camera model for each pedestrian according to the most recent tracked location. The experiments and detailed analysis are conducted on challenging data. The results demonstrate that the improvement in tracking performance is significant when a group structure is integrated.