A target block feature extraction method is proposed to reduce the interference of background information around the target. The features from the blocks in the tracking box are assigned different weights according to their locations to weaken background influence. Features with good discrimination are adaptively selected to train the classifier using the Bhattacharyya distance of the probability distribution of positive and negative samples for improving classifier robustness. The classifier may obtain incorrect information if it continues learning when the tracking object is largely occluded. Thus

a target occlusion detection approach that uses target and local background information is proposed to track successfully when occlusion occurs. Compared with five state-of-the-art algorithms on six challenging sequences

the proposed algorithm has an average success rate of 90% and 0.088 6 seconds per frame. Experimental results show that the proposed algorithm has good performance and can track successfully and efficiently for many complicated situations

such as swift movement

object deformation

complex background

and occlusion and illumination variation.