Research On Cross-camera Vehicle Tracking Technology In Traffic Scenes

The application of surveillance cameras in traffic scenes has become widely popular,generating a large amount of traffic video data.These video data require in-depth information mining to achieve deeper applications.At present,most traffic video analysis researches focus on the independent scene of a single surveillance camera,and do not make full use of the temporal and spatial correlation between cameras for comprehensive analysis and decisionmaking.Aiming at the low efficiency of multi-target tracking algorithms in practical application scenarios and poor target tracking accuracy under severe occlusion,this paper focuses on the research of multi-target tracking under single camera and cross-camera vehicle tracking technology.The main contents are as follows:(1)Research on single-camera vehicle multi-target tracking technology.Aiming at the low efficiency of multi-target tracking algorithm under single camera,it is difficult to meet the realtime problem in engineering,and two multi-target tracking algorithms are designed.One is a multi-target tracking algorithm based on optical flow and Kalman filtering.This algorithm designs a multi-dimensional constraint target trajectory association strategy,uses the median optical flow algorithm to quickly obtain vehicle speed,and combines the target obtained by Kalman filtering.Predict the position and realize the fast tracking of multiple targets across frames.The other is the multi-target tracking algorithm based on Fair MOT.This algorithm analyzes the reasons for the instability of the features obtained by the Re ID branch,fully considers the lack of channel dimensions,and introduces the SE attention mechanism to achieve higher-precision multi-target tracking.The effectiveness of the two multi-target tracking algorithms is verified by experiments,and a comparative analysis is carried out.(2)Construction of cross-camera tracking dataset in tunnel scene.By analyzing the deficiencies of publicly available cross-camera tracking datasets in traffic scenes,a trajectorylevel annotation method based on multi-object tracking is designed.This method uses a singlecamera multi-target tracking algorithm to obtain the initial labeling result of the trajectory,and uses the Ultimate Labeling labeling tool to label the same vehicle under multiple cameras.Finally,a cross-camera tracking dataset in tunnel scenes was constructed and compared with other cross-camera tracking datasets.(3)Research on cross-camera vehicle tracking technology in tunnel scene.Aiming at the problem of low tracking accuracy caused by illumination and occlusion of cross-camera vehicles in tunnel scenes,a cross-camera tracking scheme based on single-camera tracking trajectory is proposed.The program uses Kalman filter to predict the trajectory to obtain the vehicle spatial position features,and combines the residual network based on the SGE attention mechanism to obtain the vehicle re-identification features.Then,these two features are used as trajectory association clues,and a multi-feature trajectory association mechanism is designed to realize the continuous tracking of vehicles across cameras in a tunnel scene.The cross-camera tracking technology studied in this paper can be applied to the analysis and monitoring of vehicle behavior in the traffic monitoring system,and can better solve the problems of large-scale continuous vehicle motion state analysis.