Part-based 3D multi-person tracking using depth cue in a top view

While the problem of tracking 3D human motion has been widely studied, the top view is never taken into consideration. However, for the video surveillance, the camera is most of the time placed above the persons. This is due to the human shape is more discriminative in the front view. We propose in this paper a markerless 3D human tracking on the top view. To do this we use the depth and color image sequences given by a kinect. First a 3D model is fitted to these cues in a particle filter framework. Then we introduce a process where the body parts are linked in a complete 3D model but weighted separately so as to reduce the computing time and optimize the resampling step. We find that this part-based tracking increases the accuracy. The process is real-time and works with multiple targets.