Video-based Vehicle Speed Measurement Method Using Multiple Intrusion Lines

To improve the accuracy of vehicle speed detection in videos, this paper proposes a video-based vehicle speed detection method based on multi-intrusion lines. In the method, the multiple intrusion lines with known relative distance in the video are established, and the frames is detected when the vehicle passes through each intrusion line. The vehicle speed is calculated by the probability density function model which is generated by combining the frames, the sampling time of the camera, and the distance among intrusion lines. The performance of the model is verified by building simulation environment. The results show that the performance of the model can be improved by reducing the sampling time of the camera and increasing the number of intrusion lines and the distance among the intrusion lines. The method can decrease the error rate of detecting vehicle speed under different detection conditions. The Deepsort+YOLOv5 target tracking algorithm is used to realize the speed detection of the vehicle in the video. At the same time, the method is compared with the mainstream video-based vehicle detection methods on the BrnoCompSpeed comprehensive dataset. The results show that the average error rate obtained by the method is 1.40%, which is lower than the mainstream video-based vehicle speed detection methods. Copyright © 2022 by Science Press.