An Algorithm for Automatic Traffic Incident Detection Based on Surveillance Video
In order to fully utilize the effective information provided by the existing traffic monitoring network and improve the efficiency of rescue,responsibility identification and traffic diversion after traffic accidents,a simplified YOLOv3 object detection network is used as the data source to achieve real-time multi-objective detection of vehicles and pedestrians in monitoring videos.In terms of multi-target tracking,in order to improve the real-time performance and stability of the deepSORT algorithm,a feature description matrix based on undirected graphs is proposed,which performs deepSORT cascade matching by extracting secondary features of vehicle class targets.A traffic automatic incident detection algorithm is designed based on the judgment of traffic spatio-temporal conflicts,which screen and process the motion characteristics of each traffic participant,achieving automatic recognition of traffic accidents and automatic extraction of accident features.The result shows that(1)by extracting data from over 50 different angles of surveillance videos and manually annotating over 13 000 training data,the simplified YOLOv3 network can achieve an average recognition accuracy of over 80%for 8 common traffic elements,including cars,buses,pedestrians,etc;(2)automatic accident extraction testing is conducted on 50 typical traffic accident cases,achieving an 80%recognition success rate and an 8%false alarm rate.The extracted accident information includes not only simple information such as the time and location of the accident,but also high-dimensional motion characteristics such as the speed and trajectory of all parties involved in the accident,as well as video clips.It is of great significance for achieving remote and rapid processing and rescue of traffic accidents,alleviating congestion pressure caused by traffic accidents and other aspects.
traffic safetyautomatic incident detection(AID)multi-object detection and trackingsurveillance videoYOLOv3
NETL
NSTL
万方数据
