In order to solve the common problems of occlusion,rotation and backg round clutter in target tracking,a context-aware target tracking algorithm wit h re-detection mechanism is proposed in this paper.To solve the problem,first o f all,the context information is introduced on the basis of the correlation fit ering algorithm,with the purpose of making the filter to enrich the sample info rmation.Furthermore,the context-aware correlation filter is constructed to im p rove the learning ability of the filter.And then,a re-detection mechanism is i ntroduced to judge the reliability of the detection result,so that the problem of model contamination in the case of occlusion can be solved.Finally,,the per formance of the proposed algorithm is tested on the public datasets and compared with the five algorithms,including DSST (Accurate Scale Estimation for Robust V isual Tracking),Staple (Complementary Learners for Real-Time Tracking),SRDCF (L e arning Spatially Regularized Correlation Filter for Visual Tracking),TLD (Track ing-Learning-Detection) and BACF (Learning Background-Aware Correlation Filte r fo r Visual Tracking).The experimental results show that the algorithm in this pap er has better tracking robustness in complex scenes such as occlusion,rotation and background clutter.The tracking accuracy and success rate of the algorithm have reached 0.748and 0.836respectively,which are better than the other five tracking algorithms.