Object tracking with part-based discriminative context models
Object tracking is a classic problem in computer vision. Part-based appearance model has been applied to object tracking and shown good performance. However, how to initialize the parts is still an open question. In this paper, we believe that the selection of discriminative parts and effectively modeling the structural context information could improve the tracking performance. Therefore, we tackle the tracking problem by discovering discriminative parts through exemplar-SVM in the initialization, and then exploit the structural relationship between discriminative context parts and the object in the process of tracking, which is consensual in the spatio-temporal domain. Experimental results demonstrate that our approach outperforms state-of-the-art trackers on benchmark videos.