Abstract
With the rapid development of highway traffic systems, the accident rate of highway remains high. Once an accident occurs, it will cause serious casualties. In this paper, we proposed a highway accident identification method for real-time accident detection through high-speed monitoring. The proposed method focuses on high detecting speed and high recall for car accident. The designed system consists of four parts: first, yolov3 is used to detect the car. Second, identified car frames are put the identified car frame into the Mosse Tracker for tracking. Third, speed and future center point are estimated according to its size and pixel movement. If the speed exceeds a certain threshold or the future center points among other trackers are too close, the object frame will be put into a trained SVM model. Finally, the flow vector estimation calculation will be used to determine whether there is an accident. The system has fast detection speed, high recall and strong real-time performance. Through this system, we can send warning information to upstream car drivers and traffic management personnel to improve the efficiency of accident handling and reduce the rate of highway traffic accidents and casualties.
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