Multi-object Tracking Method For Pedestrian In Complex Scenes

Governments and enterprises pay increasing attention to the application of computer vision with the development of computer applied technology.Multi-target tracking,as a challenging task in computer vision,plays an important role in many fields such as intelligent transportation,intelligent monitoring and autonomous driving.Multi-target tracking is to track multiple targets in a certain scene and will face many difficulties due to the variability and complexity of the scenes and the various changes of the tracked target itself.Multi hypothesis tracking,which is data association module of the multi-target tracking method for pedestrians in complex scenes,is formed to delay the determination of a potential target object when a target object corresponds to multiple potential target objects.The multi hypothesis tracking is difficult to implement(NP-Hard)in reality.By categorizing the trajectory hypotheses and omitting one type of the trajectory hypotheses,the MHT problem can be transformed into a bipartite graph matching problem,and then use the Hungarian algorithm can realize the polynomial time approximation of MHT.In addition,the multi-hypothesis tracking theory is systematically sorted out,and is summarized into four key steps:(1)Creation and update of the trajectory tree;(2)Ttrajectory hypothesis confidence calculation;(3)Global hypothesis generation;(4)Ttrajectory tree pruning.Deformable CNN has greatly enhanced the ability to handle geometric changes compared with ordinary CNN for the image perception range of the deformable CNN has been expanded,but this will also make the network output interferenced by background noise.In this regard,an optimized scalar is introduced to optimize the convolution and pooling modules of the deformable network,which is the detection module of the multi-target tracking method for pedestrians in complex scenes,appropriately reducing the influence of irrelevant background information.Feature combination is proposed to further reduce the interference of background environment information on pedestrian detection and a simpler and faster central network is applied to the description of pedestrian detection.Experiments on 2DMOT2015,MOT16 and MOT20 datasets have found that the proposed method can handle the partial occlusion of pedestrians,and can distinguish the target object and the background environment in different and complex scenes?The experimental results show the effectiveness and practicability of the multi-target tracking method for pedestrians in complex scenes,and the processing speed of this method has also achieved good grades.