Towards vision-based 3-D people tracking in a smart room

This paper presents our work on building a real time distributed system to track 3D locations of people in an indoor environment, such as a smart room, using multiple calibrated cameras. In our system, each camera is connected to a dedicated computer on which foreground regions in the camera image are detected. This is done using an adaptive background model. These detected foreground regions are broadcasted to a tracking agent, which computes believed 3D locations of persons based on the detected image regions. We have implemented both a best-hypothesis heuristic tracking approach as well as a probabilistic multi-hypothesis tracker to find the object tracks from these 3D locations. The two tracking approaches are evaluated on a sequence of two people walking in a conference room recorded with three cameras. The results suggest that the probabilistic tracker shows comparable performance to the heuristic tracker.