Real-time monitoring of traffic parameters

Journal of Big Data

Irina Alferova¹  Kirill Khazukov¹  Tatiana Karpeta¹  Ivan Slobodin¹  Vladimir Shepelev¹  Irakli Charbadze¹  Salavat Shabiev¹ 

[1] South Ural State University, 454080, Chelyabinsk, Russia;

关键词: Neural network;    YOLO v3;    Data for training the neural network (Dataset);    Traffic flow assessment;    Vehicle detection;    Vehicle classification;    Vehicle speed;    Traffic monitoring;   

DOI  :  10.1186/s40537-020-00358-x

来源: Springer

【 摘 要 】

This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

【 授权许可】

CC BY