Robust Object Tracking via Information Theoretic Measures

Object tracking is a very important topic in the field of computer vision. Many sophisticated appearance models have been proposed. Among them, the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of noise. However, in practice, the tracked objects are often corrupted by complex noises (e.g., partial occlusions, illumination variations) so that the original appearance-based trackers become less effective. This paper presents a correntropy-based robust holistic tracking algorithm to deal with various noises. Then, a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective function. Based on the proposed information theoretic algorithm, we design a simple and effective template update scheme for object tracking. Experimental results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.