Research On Vehicle Detection And Tracking Method Based On Deep Learning

In today’s society,the economy is developing rapidly.At the same time,the living standards of the people are also continuously improving.The car has become an indispensable means of transportation for every household,and the rapid increase in the number of vehicles has also led to the frequent occurrence of traffic accidents.In order to ensure the safety of people’s lives and property,advanced auxiliary driving system,automatic driving technology and intelligent transportation technology have become the focus of scholars and various relevant institutions.Target detection and tracking technology is the basis of intelligent transportation technology,so this paper studies a real-time and accurate target detection and tracking algorithm.The research content of this paper can be divided into the following three points:(1)Among the commonly used target detection algorithms,the traditional target detection algorithm has high time complexity and poor real-time performance,which is not suitable for the detection of road vehicle targets in this article;in the R-CNN series algorithm based on deep learning,SSD algorithm and YOLO Among the series of algorithms,the YOLOv3 algorithm basically balances the relationship between accuracy and real-time.YOLOv3 is selected as the target detection algorithm in this article and improved to meet the real-time and accuracy of road vehicle detection.This paper analyzes all kinds of target tracking algorithms.Considering the multi-target tracking of road vehicles under the condition of surveillance video,the deep sort target tracking algorithm is selected.(2)The feature extraction network in the YOLOv3 algorithm is improved,and the Darknet-53 feature extraction network structure is reduced and replaced by the Darknet-33 feature extraction network structure to reduce the complexity of the algorithm and improve the speed of detection;In order to improve the information transmission ability of Darknet-33 feature extraction network,dense connection network is added to improve the detection accuracy;Also for the situation that the road vehicles are obscured by other vehicles or obstacles during driving or poor lighting conditions caused by night driving,the parameter correction unit PRe LU activation function is used to replace the original Leaky Re LU activation function.(3)The detection part of Deep Sort algorithm is changed to the improved YOLOv3 target detection algorithm in this paper,and the Deep Sort algorithm is improved for the characteristics of road vehicles driving.In view of the situation that the vehicle encounters and separates during driving,which leads to missed detection and false detection of the tracking algorithm,this paper proposes to add Mean Shift algorithm to achieve the tracking of the center position of the vehicle target;In the case of sudden acceleration or deceleration or sudden turning during the driving of the vehicle,this paper adds acceleration parameter components to the Deep Sort algorithm to capture more accurate position information.