Abstract: An anti-occlusion real time target tracking algorithm employing spatio-temporal context was proposed to solve the problems of tracking instability or even failure, which were caused by illumination variation, background clutters, target deformation or occlusion. Firstly, in the framework of spatio-temporal context model, the adaptive dimensionality reduced color features were adopted to describe the target to promote the distinguish ability in complex scene. Secondly, the peak and the peak-to-sidelobe ratio of confidence map response were combined to evaluate the target tracking status. Then, occlusion was discriminated by the correlation coefficient between target templates. Finally, when the target tracking status fluctuated, the update speed of target model was reduced, and the target coordinates were corrected by the Kalman filter. When the target was occluded seriously, the target coordinates was predicted according to the Kalman filter, and the target model was stopped to update for recapturing and tracking the target again after occlusion released. 36 color sequences with multiple challenging attributes were selected to evaluate the performance of the proposed algorithm, and it was compared with other excellent target tracking algorithms. The experimental results demonstrated that this algorithm has strong anti-occlusion ability, and improved the robustness of target tracking effectively under the influence of disturbance factors such as illumination variation, background clutters and target deformation. Meanwhile, it met the real time requirement of target tracking.