Aiming at the problems of tracking failure and large tracking error in the case of target occlusion and textures change in moving targets tracking

an improved compressive sensing（ CS） algorithm is proposed. Response threshold of a sigmoid function is used to judge whether occlusion exist

to determine whether to update classifier parameter or not

in order to avoid object model is mistake updated when heavy occlusion exists. To solve the tracking instability cause by single feature

a feature fusion method under rules that combined grey features and texture features is presented. Tracking is guided by the fused feature. Experiments verify that the improved algorithm has 17. 84 % higher success tracking rates

and 11. 59 % lower average error rate compared with traditional algorithm.