Research On Video Multi-Object Tracking Based On Fusion Of Object Detection And Data Association

In recent years,with the rapid development of the field of object detection,the video multi-object tracking algorithm based on object detection has made a breakthrough,which has a very wide range of applications in video monitoring and security,intelligent medical,automatic driving and so on.However,the tracking speed of the algorithm and how to deal with the tracking drift caused by the occlusion are still two important factors restricting its practical application.This paper focuses on these two issues,the main work is as follows.Firstly,in order to achieve a good balance between the speed and accuracy of multi-object tracking network,a video multi-object tracking algorithm is proposed,which integrates object detection and data association.At present,the video multi-object tracking algorithm based on object detection mostly regards object detection and data association as two independent parts,which leads to low efficiency of algorithm execution and difficult to meet the requirements of real-time video processing.In order to solve the above problems,this paper integrates the two into a unified deep learning network,which shares the basic features of the object and executes in parallel.Only when the final association is done,the object detection results are called for mask processing,which greatly improves the efficiency of the algorithm.Then,an anti occlusion video multi-object tracking algorithm based on time and space features is proposed,which can further improve the accuracy of multi-object tracking on the basis of ensuring the advantages of tracking speed.The appearance feature of the object is an important basis for data association,while occlusion will destroy the extraction of appearance feature and easily cause tracking drift.In order to solve this problem,in time and space,based on the multiple attention mechanism,this paper enhances the appearance features of the object extracted from the backbone network,increases the proportion of the non occluded position,so as to make the data association more accurate and improve the tracking performance of the algorithm.Finally,experiments are carried out on three open data sets mot15,mot16 and mot17 respectively,and a variety of advanced algorithms are compared.The results show that the algorithm not only realizes the efficient tracking of multiple objects,but also ensures the tracking accuracy at the leading level,with high practical application value.