The correlation filtering algorithm determines the target position by the similarity between the template and the detection target. Since the related filtering concept is used for target tracking, it has been widely concerned, and the proposal of the kernelized correlation filter is to push this concept to a new height. The kernelized correlation filter has become a research hotspot with its high speed, high precision and high robustness. However, the kernelized correlation filter has serious defects in anti-blocking performance. In this paper, the algorithm for the anti-occlusion performance of kernelized correlation filter is improved. An improved KCF algorithm based on Sobel edge binary mode algorithm is proposed. The Sobel edge binary mode algorithm is used to weight the fusion target feature. The target's peak response intensity sidelobe value is more than the detection target is lost. Finally, the Kalman algorithm is used as the target occlusion strategy. The results show that the proposed method not only has better robustness against occlusion, but also satisfy the real-time requirements and can accurately re-tracks the target.