Occlusion is an essential factor that often leads to the failure of object tracking. Improving antiocclusion performance of the algorithm has been a research hotspot in tracking. First, this paper analyzes why occlusion easily leads to tracking failure. Furthermore, the importance of constructing a strong discriminant and robust object model to improve the antiocclusion performance of the tracking algorithm and an effective scheme to improve the antiocclusion performance of the target model are discussed. Then, based on the utilization information type of constructing object model, the representative methods with better antiocclusion performance are divided into three categories on the basis of effective feature, state estimation, and stable spatiotemporal informations. Further, the antiocclusion idea scheme, suitable occlusion scene, pros and cons, and improvement schemes of object tracking algorithm based on Kalman filter, particle filter, local spatial information, time context information, and spatiotemporal context information are analyzed in detail. Finally, through performance comparison with the tracking performance of different types of methods in occlusion scenarios, the antiocclusion effectiveness of the object model construction scheme is analyzed. The application and development direction of learning semantic information lightweight network design, scene context prediction, and bionic vision mechanism are presented.