Automatic multi-head detection and tracking system using a novel detection-based particle filter and data fusion

We present a novel automatic system integrating head detection with particle filter for realtime multi-head tracking (MHT) in video. Distinct with the conventional particle filter which gets particles from the prior density, we propose a novel importance function based on the up to date detection and motion observation which makes the particles more effective and helps us to achieve stable tracking by using much less particles. We also propose a general likelihood model in the context of MHT. Different information can be fused in a principle manner to make the tracker more stable. The proposed approach can handle not only the changes of scale, lighting, zooming, and pose, but also fast motion, appearance, and hard multi-head occlusion.