In order to solve the problem of tracking failure caused by background interference and multi-peak response of kernel correlation filtering（ KCF） visual tracking algorithm

a multi-channel fusion feature visual tracking algorithm is proposed. The algorithm fuses the color channel feature（ CN） and the direction gradient histogram（ HOG） feature of the target into new features

and combines the principal component analysis（ PCA）method to ensure alignment with the original KCF features. In order to solve the problem of multi-peak response interference

a new multi-peak detection method is proposed to avoid drift of model tracking. According to the results of the OTB50 experiment

the proposed algorithm has better accuracy and robustness than other algorithms

and can improve the performance of the target tracking algorithm.