Real-time monitoring of traffic parameters

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...

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Bibliographic Details
Published in:	Journal of big data Vol. 7; no. 1; pp. 1 - 20
Main Authors:	Khazukov, Kirill, Shepelev, Vladimir, Karpeta, Tatiana, Shabiev, Salavat, Slobodin, Ivan, Charbadze, Irakli, Alferova, Irina
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 06-10-2020 SpringerOpen
Subjects:	Communications Engineering Computational Science and Engineering Computer Science Data for training the neural network (Dataset) Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Mathematical Applications in Computer Science Networks Neural network Traffic flow assessment Vehicle classification Vehicle detection YOLO v3 Traffic flow assessment Vehicle speed Vehicle detection Vehicle classification Traffic monitoring YOLO v3 Neural network Data for training the neural network (Dataset)
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Summary:	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.
ISSN:	2196-1115 2196-1115
DOI:	10.1186/s40537-020-00358-x