Analysis of the Occlusion Interference Problem in Target Tracking

Abstract

As an indispensable part in the field of computer vision, target tracking has been widely used in intelligent transportation, missile guidance, unmanned aerial vehicle (UAV) tracking, and many other fields. It has become one of the hot directions in computer vision in recent years, while occlusion problem has always been a great difficulty and challenge in the process of target tracking. In this article, the problem of occlusion interference in target tracking is described, and the solution of occlusion problem is proposed based on different occlusion conditions. Due to the disadvantages of feature point center weighting, multiparticle template matching, and Kalman filter trajectory prediction algorithms in different cases, some algorithms with higher robustness and stability are developed to solve the occlusion problem. In the analysis of the anti-occlusion model, it is found that some tracking errors caused by occlusion can be solved by improving the quality of negative training samples and enriching the diversity of positive sample sets. According to the different training characteristics of online and offline tracking algorithms, the anti-occlusion model suitable for an active learning algorithm under different tracking conditions is found, and the tracking algorithm and characteristics of the active learning algorithm are listed, which is helpful to select the suitable tracking model in different scenarios. Finally, the future development of occlusion problem in target tracking is prospected.