Research On Vehicle Tracking Algorithm Based On Deep Learning

As urbanization process continues to accelerate,the rate of private car ownership continues to increase,the car driving process produces a large number of traffic management,route planning,vehicle transportation and other behavioral information,for the traffic information services,traffic management,transportation safety and other services such as intelligent and efficient system to provide the basic data.Traffic control and decision-making,real-time road conditions in navigation software,automated vehicle control and other key systems are dependent on the vehicle operation data of road nodes.Vehicle target tracking plays an important role in the intelligent transportation system.With the application of deep learning in machine vision,various computer vision-based algorithms are used in large numbers for target detection and tracking,and how to efficiently and accurately detect and track vehicles has become a hot research topic in this field.As the actual road conditions are prone to the presence of more vehicles when the distance from the camera is far,while the pixel share in the picture or video is relatively small,etc.leading to low detection and tracking accuracy and how the vehicle tracking algorithm achieves the best balance of accuracy and speed,the specific work done in this paper to address the above issues is as follows.(1)This paper proposes a target detector based on the improved YOLOv4(You Only Look Once v4)algorithm,which replaces the K-means algorithm by the dichotomous k-means clustering algorithm to obtain more representative prior frames,thus enabling more targeted training and can accelerate the convergence of the model.In the YOLOv4 network,the target features are passed through the bottom-up convolution operation,but due to the repeated convolution operation,the data about the small targets and the features and location information of the occluded targets in the target features will be gradually lost.In response to the above existing problems,this paper improves the PAN in YOLOv4,in which the upper layer of CSPDarkNet53 This paper improves the PAN in YOLOv4 by jump splicing the features output from CSPDarkNet53 with the next layer to increase the feature detection scale,thus preserving the features of different layer spaces in the high level feature space,scenarios by 1.97%and the recall rate by 1.88%-4.03%compared to YOLOv4 and 2.24%in mixed scenarios.(2)Since DeepSort’s deep appearance model is trained on the human re-identification dataset,this paper modifies the DeepSort appearance feature extraction network for the actual characteristics of the vehicles,and also modifies the input of the network model according to the appearance features of the vehicles.The above two experimental parameters are modified to make it more suitable for performing vehicle target tracking.(3)DeepSort target tracking algorithm will match the apparent features of the target vehicle in the nearest neighbor during the real-time target tracking process,and will perform feature extraction and saving and comparison after each frame through tracking and cascade matching,which will reduce the speed of target tracking because it will take a lot of time to perform feature extraction and saving and comparison in each frame.And Sort target tracking algorithm uses simple Kalman filter and Hungarian matching for performance at high frame rate,but ignores the surface features of detected objects,which will affect the tracking accuracy if occlusion occurs between objects.In this paper,based on the analysis of vehicle occlusion in the dataset,we propose to mix DeepSort algorithm with a certain percentage of Sort algorithm for vehicle tracking.Through corresponding experiments,it is confirmed that the proposed method improves the FPS with almost no loss of accuracy.Finally,compared with other vehicle tracking algorithms,the improved DeepSort algorithm proposed in this paper while using the improved YOLOv4 as a detector for vehicle tracking improves both the accuracy of tracking,the precision of tracking and the speed of vehicle detection,so that the best balance of accuracy and speed can be achieved.