For long-term robust tracking to single target, a corrected tracking algorithm based on static-adaptive appearance model was proposed. Firstly, the interference factors that may be encountered in the tracking process were divided into two categories from environment and target itself, then a static appearance model and an adaptive appearance model were proposed respectively. The static appearance model was used for global matching while the adaptive appearance model was employed for local tracking, and the former corrected tracking drift of the latter. A single-link hierarchical clustering algorithm was used to remove the noise introduced by the fusion of the above two models. To capture the re-occurring target, static appearance model was applied for global search. Experimental results on standard video sequences show that the accuracy of tracking the target center is 0.9, and the computer can process 26 frames per second. The proposed tracking algorithm framework can achieve long-term stable tracking with good robustness and real-time performance.