Control Systems and Computers, N3, 2020, Article 5

Control Systems and Computers, 2019, Issue 3 (287), pp. 50-59.

UDC 004.932

O.P. STELMAKH, PhD student of the Department of Computer-Aided Management and Data Processing Systems, National Technical University of Ukraine “Igor Sikorsky Kyiv Politechnic Institute”, 03056, Kyiv, Peremohy Ave 37, Ukraine,
stelmahwork @gmail.com

I.V. STETSENKO, Doctor of Science, professor of the Department of Computer-Aided Management and Data Processing Systems, National Technical University of Ukraine “Igor Sikorsky Kyiv Politechnic Institute”, 03056, Kyiv, Peremohy Ave 37, Ukraine,
stiv.inna@gmail.com

D.V. VELYHOTSKYI, Junior Researcher, Institute of Applied Problems of Physics & Biophysics, NAS of Ukraine, 03680, Kyiv, 3 V.Stepanchenko str., Ukraine,
vely_dima@ukr.net

INFORMATION TECHNOLOGY OF VIDEO DATA PROCESSING FOR TRAFFIC INTENSITY MONITORING

Traffic jams are a huge problem for all road users and are caused by increasing traffic intensity and poor quality of traffic management systems. Systems that control traffic flows and decide to change control parameters must receive reliable and up-to-date data on traffic intensity. In order to accurately determine the traffic intensity, a system of automated video data processing from video surveillance cameras of the traffic lane is developed. The traffic intensity is determined by the developed method of obtaining the traffic congestion coefficient (TLCR) according to the data, obtained by processing the video frame using the U-Net neural network, and the following transformation of TLCR time series into traffic intensity time series. The newinformation technology implements an image processing algorithm to detect the presence of vehicles in a certain section of road, a method of determining the congestion of the lane (TLCR) and a method of determining the intensity of successive values of congestion of the lane.The experimental results show that the proposed information technology is able to identify traffic intensity with an accuracy of 99.35 percent.

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Keywords: Image Analysis, Traffic Intensity, Traffic Congestion Index, TLCR, Neural Network.

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Received 25.05.2020