Abstract:

The Mean Shift algorithm has a defect in handling moving targets with large scale change or being obscured. In order to solve this problem, we propose a bandwidthadaptive and antiblocking tracking algorithm based on multi-level square matching and fragment. The proposed algorithm uses the centroid deviation of the target model and the bandwidth trials method to compute the possible scales. The motion trend of the target is predicted through the multilevel square matching method, and the scale of the largest Bhattacharyya distance of the candidate targets is selected as the new bandwidth of the Mean Shift kernel function. At the same time, we divide the target into several fragments, adaptively change their weights according to the degree of being obscured, and then fuse the results of effective fragments under certain rules. Experimental results show that this algorithm has good robustness performance on tracking targets.

Key words: Mean Shift;object tracking;multi-level square matching;fragment