A robust part-based tracker

In this paper, we propose a new method for modeling appearance variances in generic object tracking task. Although object tracking has been studied by many researchers for a long time, there are still many challenging problems, which is mainly due to the complex variances of object's appearance. While most of traditional methods using a global or pixel-wise approach, we proposed a part-based tracking framework. We divide an object region into several non-overlapping parts (note they are not semantic as limbs and head of a human), and then a local classifier is updated on-line for each part. We gain a global confidence map by applying these local classifiers to the next frame, and find the new location of target object, i.e. the peak of confidence map, using mean-shift. Our tracker runs real-time, and is robust to some kinds of appearance variance (e.g. change of illumination, occlusion, change of pose, deformation of shape, object/camera movement and so on). Experiments show that our method outperforms the other states of the art approaches, especially on dealing with occlusion.