Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance System

With the rapid development of intelligent video surveillance system for transportation, traditional single-camera-based video analysis has become insufficient. Many researches have focused on the non-overlapping multi-camera target tracking. However, the tracking precision and the computing overhead are still big obstacles. This paper proposes a novel vehicle tracking approach for non-overlapping multi-camera targets with data fusion by using minimum cost and maximum flow method. Structured information of moving targets is extracted and associated with other information such as motion time, the topology of camera network to solve targeted vehicle tracks. Besides, to improve the performance of the process, a parallelization algorithm for camera network topology partitioning is presented, which makes it possible for each sub-graph to track target independently in parallel. The experiment results show that the presented approach is able to perform target vehicle tracking analysis with high efficiency and accuracy.