Aiming at the problem of object tracking failure caused by occlusion and out of view in correlation filter tracking,we propose an anti-occlusion optimization algorithm based on background aware correlation filters (BACF).First,the feature of histogram of oriented gradient (HOG) and color name (CN) are fused in the feature extraction part;Secondly,a cascaded occlusion optimization algorithm is proposed to determine whether the target appearance has changed significantly during the object tracking process based on the average peak correlation energy (APCE) and the maximum response peak,and further calculate the similarity of features between the target neighboring filter templates by the Bhattacharyya distance to determine the real cause of the target appearance change,and then decides whether to update the template;For the problem of target out of view,a template inspection strategy is proposed and the confidence of the filter template is checked every K frames during the tracking process,and if the target is lost,a new template is generated and the target is recaptured by a global search.By testing on the object tracking dataset OTB2015,the accuracy and success rate of the optimized algorithm are 83.0% and 78.8%,respectively,which effectively improve the performance of the algorithm.