Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: 2nd International Scientific Conference on Advanced Technologies in Aerospace, Mechanical and Automation Engineering, MIST: Aerospace 2019
Год издания: 2020
Идентификатор DOI: 10.1088/1757-899X/734/1/012156
Аннотация: The paper investigates the evaluating task of the traffic as the registered number of vehicles of one of the three classes (a bus, passenger car or truck) that passed in a certain direction per unit of time, according to data from CCTV cameras in real time. For simplification this problem solving was divided into three components. They are registration of traffic flow objects and their classification, finding trajectories of fixed objects and determining traffic lanes, as well as counting objects in each direction. A comparative analysis of the existing algorithms, software and hardware systems that solve problems similar to the problems was carried out; also, a proprietary approach was proposed. The analysis describes the feasibility for developing their own approach due to the lack of an algorithm that meets all the requirements, such as estimating the traffic on the basis of data from surveillance cameras. Besides, the existing hardware and software systems are of the high cost and complexity. The YOLO artificial neural network was used to recognize objects on the frame in the form of rectangles containing the image of a vehicle. To build tracks, objects are compared frame-by-frame based on a set of characteristics, including a simple (average) perceptual hash and the coordinates of the corresponding rectangle. The developed algorithm for constructing trajectories of moving traffic objects, highlighting lanes of automobile traffic and counting vehicles is proposed as a software module. The testing results concerning the quality of the algorithm are introduced. The main advantages and disadvantages of the proposed approach are highlighted. © Published under licence by IOP Publishing Ltd.
Журнал: IOP Conference Series: Materials Science and Engineering
Выпуск журнала: Vol. 734, Is. 1
ISSN журнала: 17578981
Издатель: Institute of Physics Publishing 012156
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