Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the problem that the poor tracking accuracy of correlation filter algorithm under occlusion condition, this papre proposed a kernelized correlation filter tracking method using joint multiple blocks estimation. Firstly, it divided the target into several blocks adaptively according to the geometric features of the initial frame tracking box, and each block using the KCF method for tracking independently to get a combined confidence map. Then it took the location and scale of previous frame target as priori information sampling the search area, meanwhile, the weight density of the confidence map in the sample box is used as the observation value, achieve optimal estimation the candidate target using particle filter algorithm. Finally, blocks with lower confidence levels back-project to previous frame for occlusion detection to prevent template update mistakenly. The qualitative and quantitative experiment results shows that compared with the original KCF algorithm, the tracking accuracy of the proposed method improves about 10% and it is robust to occlusion and scale change in some degree.
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