The traditional correlation filtering algorithm is improved to improve the tracking performance of the algorithm in complex scenes such as scale change and occlusion deformation. In this paper, a robust tracking algorithm combining scale adaptation and re detection mechanism is proposed. Based on the fusion of FHOG and CN complementary features, a scale adaptive strategy is introduced to solve the problem of scale change. In addition, the model updating strategy is further optimized and the re detection mechanism is added to enhance the robustness of the algorithm. The otb100 dataset test results show that the accuracy and success rate of the proposed algorithm are improved by 4.9% and 17%, respectively, compared with KCF algorithm. The average tracking speed is 45 frames / s, and the performance is excellent in occlusion, scale change and illumination change scenes, which can effectively achieve long-term target tracking.