Research And Implementation Of Multi-object Vehicle Trajectory Continuous Tracking And Traffic State Recognition Algorithm Based On Cross-camera

Cross-camera vehicle tracking is an important field in intelligent transportation system and intelligent visual perception of smart city.Cross-camera vehicle tracking refers to tracking vehicles in a large area within the range of multiple cameras,which has a wide range of applications in intelligent transportation,security monitoring,crowd analysis,urban traffic management and other aspects.This paper focuses on the problem of vehicle target detection and tracking in real scenes and cross-camera vehicle identity association,obtains the complete trajectory information of vehicle operation,and then carry out traffic status recognition,providing data basis for intelligent transportation.The main research content of this paper is divided into the following four points:(1)For real traffic scenes,this study collected and produced dataset for cross-camera vehicle tracking and traffic status recognition,including three training videos with 3000 frames and three test videos with 1500 frames.The training set and the test set were collected over different periods of time and included a total of about 417 K vehicles.It includes vehicle location,identity,status and other complete information for model training and algorithm evaluation.(2)According to the data characteristics of traffic scenes,the vehicle target detection model and vehicle target tracking model within a single camera are constructed respectively.Following the detection and tracking paradigm,this paper estimates vehicle location information in traffic scene video based on the Faster R-CNN target detection network.Based on the results of vehicle object detection and vehicle appearance feature information,the Deep SORT vehicle tracking algorithm was used to extract the vehicle trajectory in a single camera on the real road.The experimental results show that the target detection and tracking algorithms in a single camera show good performance in different cameras,which provides the basis for the accuracy of the complete trajectory.(3)For the identity matching process of cross-camera vehicles,the measurement method of integrating spatio-temporal information and appearance information is proposed.The spatialtemporal information is extracted based on the track encoder,and the appearance information is extracted by the image encoder.The fusion measurement method of those can effectively reduce the problem of excessive changes in the appearance information of vehicles in the identity matching process,and reduce the index space in the matching process by using the inherent traffic rules.Experimental results show the effectiveness of the matching algorithm,which can correlate multiple vehicle trajectories in multiple cameras and realize cross-camera vehicle tracking.(4)Identify the traffic state based on the complete trajectory of vehicles in multiple cameras,including the estimation of traffic flow parameters at the macro level and the analysis of abnormal vehicle behavior at the micro level.Through experimental verification,it is found that the proposed algorithm can accurately estimate traffic flow parameters and vehicle behavior recognition,complete the perception of vehicle running state in traffic scenes,and provide data support for traffic control and analysis.