Adaptive hierarchical block tracking method in case of partial occlusions

In order to solve the tracking problem occurred during occlusions, an adaptive hierarchical block tracking method is proposed after analyzing the changes of the target characteristics under partial occlusions. Firstly, color histogram features are selected to describe the target. The similarity between the target model and the candidates is measured by the Bhattacharyya coefficient, which can also be used to evaluate the degree of occlusions. The object is divided into four blocks when it is occluded, and the mean shift procedure is used to track each block separately. Then, according to the value of the Bhattacharyya coefficient, the partially occluded block is found and divided into four sub-blocks, which are tracked by block matching algorithm separately. Finally, the information of all the blocks is used to determine the displacement vector of the target. Experimental results show that compared to the traditional mean shift tracking method, this method can make full use of the features of the unoccluded sub-blocks, improve the tracking accuracy and solve the target tracking problem in case of partial occlusions.