In this paper, the algorithm of Kernel Correlated Filtering (KCF) target tracking is deduced in detail. Secondly, aiming at the single feature extracted by KCF algorithm, which can not express the appearance model of the target well, a method of fusing multiple features is proposed to increase the distinguishability of the appearance model. For the problem that KCF algorithm cannot adapt to scale change, a scale adaptive change method is introduced. To tackle the problem that the fixed update rate of KCF algorithm learns error information when the target is occluded, an online model updating factor method is proposed. Finally, the experiment results show that the proposed algorithm has higher tracking accuracy and stronger robustness in the case of large changes in target scale and occlusion.
