To overcome the problem of loss of target caused by fast motion and the issue of partial occlusion in the tracking of kernel correlation filters, this paper proposed a new kernel correlation tracking algorithm that combines adaptive template updating and the prediction of the relocation of a target, based on the scale adaptive with multiple features tracker (SAMF). A template updating mechanism that combines target velocity and feature changes was proposed to improve the adaptability to fast movement of the target. Based on cooperative tracking of long time and short time filters, a target position correction and relocation model was proposed to improve the ability of the tracker to cope with partial occlusion of the target. In 100 sequences of OTB-2015 video set, the proposed algorithm was compared with the algorithms based on sequence sets and the SAMF algorithm. The tracking accuracy of the proposed algorithm is 2% higher than that of the SAMF algorithm, and the success rate is increased by 1%. The proposed algorithm has better tracking ability for fast moving targets and the target relocation scheme effectively addresses the problem of partial occlusion of the target.