Research On The Methods Of Solving The Occlusion Problem In Multi-people Tracking Based On Human Matching

With the development of computer vision, human tracking technology has become a heated topic, which has been increasingly widely used in practice. Single-human tracking technology has achieved great development, tracking accuracy and speed has reached a practical stage. However, multi-human tracking technology is still a difficult question, because when multiple targets being tracked, self occlusion, mutual occlusion and background occlusion often happen, resulting in lost tracking or wrong tracking.Presently, scholars at home and aboard deal with multi-human tracking by trajectory matching or dynamic motion equation to predict the target's state. If the target's trajectory is relatively simple, these methods can obtain good results. However, in most cases, the movement direction and speed of the target is uncertain, and the trajectory and state are difficult to predict, therefore, the existing methods can not solve the issue of the occlusion.This paper presents a new algorithm based on human matching to solve the occlusion problem in multi-human tracking. At the same time of tracking, each person's upper body image is extracted, and a training sample pool is created. When the target is occluded, the original body information has been recorded in the sample pool. If a new object is detected, then matching the target with the sample pool. If they match, we consider that the object belongs to the original targets, if not, it is a new object, and a new trajectory should be created.This method avoids the trajectory prediction; therefore, even if the target's movement is random, the method will also provide a good correlation result. It will let single human tracking extend to multi-human tracking freely.Through a large number of video sequences for testing, we can see that, compared to other methods, the paper's algorithm provides a better performance on correlation accuracy and a better solution to the occlusion problem in multi-human tracking. Meanwhile, two features have been applied to human matching and achieved good results, which is valuable in human recognition in the future.