Target Tracking Based on Collaborative Learning Kernelized Correlation Filter

In this paper, a tracking algorithm based on collaborative learning Kernelized Correlation Filter is proposed. According to the framework of KCF, two trackers based on HoG and CN are cooperatively trained, and independent scale adaptive algorithm is used at the same time, and we call it collaborative learning Kernelized Correlation Filter (CL-KCF) algorithm. Our experimental results show that the proposed algorithm has higher tracking accuracy and robustness than other trackers, and can automatically adapt to the target's scale change.