Real-time person counting by propagating networks flows

In this paper we present a system that tracks multiple persons by detection in real-time. We introduce a measure for similarity of detections which segments significant information from background clutter by using statistical information obtained during the learning phase of the detector. In order to track multiple persons we map the detections into flow networks utilizing this measure. A continuous real-time processing of video streams is accomplished by analyzing only small chunks of detections consecutively using different networks. By propagating the result of one network into the subsequent one a temporal consistent association is achieved. The system was evaluated using a standard video sequence containing a crowded scene and an own dataset with very long sequences. The results demonstrate that the system performs comparable to other systems while meeting real-time requirements.