Abstract: Traditional correlation filter trackers are not effective in dealing with the problem that caused by target scale changing and partial occlusion.Aiming at sloving this problem,a block tracking algorithm based on KCF was proposed in this paper.In the first step,tracking object is divided horizontally or vertically according to its apperance feature.Then,in the tracking process,local filter is used to track local block,and center point position of the global block can be predicted by the tracked result of the local blocks.At last,the final position of the target is determined by the global filter.The relevant information renewal and scale parameters are fed back to the local filters to update both global and local filter.In addition,different from KCF,which only uses HOG feature,CN feature is imported in the proposed algorithm to enhace the ability of traget deformation tracing and motion blurring tracing.Moreover,in order to solve model drift problem caused by partial occlusion,a method based on effective local block is raised to guide model updating.Criteria of evaluating effective local block also defined.Furthermore,the scale of the target can be effectively estimated by analyzing the distance between local blocks,which solves tracking failure problem caused by target scale changing.The algorithm is evaluated on the public dataset OTB-100,which contains 100 video samples.The results show that the proposed algorithm performs quite well in the situation of scale changing and partial occlusion.Compared with KCF,the accuracy of the proposed algorithm is improved by 10%,and the overall performance is better than other four KCF based algorithms.The processing speed of the algorithm reaches 32 fps.

Key words: Block, Correlation filter, Partial occlusion, Scale change, Visual tracking