Adaptive dynamic model particle filter for visual object tracking

One of the key issues related to object tracking is the representation of the object motion. It is a challenging problem because the object usually exhibits complex and rich dynamic behavior. In this paper, we propose an adaptive dynamic model to describe the dynamics/motion of the object and embed it into the particle filter framework for visual object tracking. The model characterize the object motion preciously by a switch-and-fusion strategy, which integrates both long period and short period motion information by the combination of multiple simple motion models. Experimental results demonstrate that, the proposed method achieves better results than the conventional particle filter, especially when the object moves quickly and changes the motion pattern drastically.