Compressive tracking via oversaturated sub-region classifiers

This study proposed a tracking algorithm based on oversaturated sub-region classifiers. Compared with the compressive tracking (CT), the tracker can reduce the influence of occlusion and improve the stability and accuracy of tracking result. First, the target region is divided into oversaturated sub-regions randomly, and then some sub-region classifiers are adaptively selected based on their confidence. Each selected classifier can find a candidate target position. At last, the place with the maximum candidate positions´ distribution density is the final location of the target. Experiments on different videos demonstrate that the proposed algorithm has stronger anti-occlusion ability than the CT and is more robust and stable than the traditional sub-region-based tracking algorithm.