This paper proposes a complementary tracking algorithm with high-confidence updating strategy to address target tracking problems in complex scenes such as target occlusion, deformation, rotation, illumination changes, and background interference. The algorithm is based on the core-related filter-tracking algorithm and the statistical color feature-tracking algorithm. First, the Laplacian of Gaussian operator and local binary mode are used to enhance the edge information and texture features of the target. Then, the tunable Gaussian window function and scale estimation model based on the key points optimization algorithm are introduced. Finally, the response peak value and a high-confidence updating strategy are designed for the merged rate of the tracking frame to adaptively updating the template. Experimental results show that the precision and success rate of the algorithm on the OTB2013 data set are 88.3% and 72.4%, respectively.