Integral region-based covariance tracking with occlusion detection

Covariance tracking has achieved impressive successes in recent years due to its competent region covariance-based feature descriptor. Although adopt fast integral image computation, covariance tracking's brute-force search strategy is still inefficient and it possibly leads to inconsecutive tracking trajectory and distraction. In this work, a generalized, adaptive covariance tracking approach with novel integral region computation and occlusion detection is proposed. The integral region is much faster than integral image and adaptive to the tracking target and tracking condition. The adaptive search window can be adjusted dynamically by simple occlusion detection. The integral image and the global covariance tracking can be seen just a special case of integral region and the proposed approach, respectively. The proposed approach unifies the local and global search strategies in an elegant way and smoothly switches between them according to the tracking conditions (i.e. occlusion distraction or sudden motion) which are judged by occlusion detector. It gets much better efficiency, robustness of distraction, and stable trajectory by local search in normal steady state, while obtains more abilities for occlusion and re-identification by enlarged search window (until to global search) in abnormal situation at the same time. Our approach shows excellent target representation ability, faster speed, and more robustness, which has been verified on some video sequences.