In order to improve the tracking performance of the SAMF algorithm in complex scenarios, an object tracking algorithm combining SAMF with visual saliency is proposed.Based on the SAMF correlation filter tracking framework, the reliability of the SAMF tracking results is evaluated by designing a confidence discrimination strategy.When the tracking results are identified as low confidence, the saliency detection algorithm is used to correct them, so as to realize the targets relocation to address the tracking drift caused by occlusion and other factors.Experiment results show that the proposed algorithm greatly improves the overall performance and tracking robustness of the SAMF algorithm when dealing with various interference factors, while maintaining good real-time performance.