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Information Technology of Video Data Processing for Traffic Intensity Monitoring

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. The systems that control traffic flows and decide to change parameters must receive reliable and up-to-date data on traffic intensity. In order to accurat...

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Main Authors: Stelmakh, O.P., Stetsenko, I.V., Velyhotskyi, D.V.
Format: Article
Language:English
Published: Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України 2020
Series:Control systems & computers
Subjects:
Intellectual Informational Technologies and Systems
Online Access:http://dspace.nbuv.gov.ua/handle/123456789/181186
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spelling irk-123456789-1811862021-11-07T12:41:59Z Information Technology of Video Data Processing for Traffic Intensity Monitoring Stelmakh, O.P. Stetsenko, I.V. Velyhotskyi, D.V. Intellectual Informational Technologies and Systems Traffic jams are a huge problem for all road users and are caused by increasing traffic intensity and poor quality of traffic management systems. The systems that control traffic flows and decide to change 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 method of obtaining the traffic congestion coefficient (TLCR) according to the data, gained 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 new in formation 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 of99,35 percent. Мета статті.Метою дослідження є підвищення точності визначення інтенсивності руху на основі аналізу відеоданих у режимі реального часу шляхом автоматизованої обробки відеоданих, отриманих від камер відеоспостереження у смузі. Цель статьи. Целью исследования является повышение точности определения интенсивности движения на основе анализа видеоданных в режиме реального времени путем автоматизированной обработки видеоданных, полученных с камер видеонаблюдения полосы. 2020 Article Information Technology of Video Data Processing for Traffic Intensity Monitoring / O.P. Stelmakh, I.V. Stetsenko, D.V. Velyhotskyi // Control systems & computers. — 2020. — № 3. — С. 50-59. — Бібліогр.: 16 назв. — англ. 2706-8145 DOI https://doi.org/10.15407/usim.2020.03.050 http://dspace.nbuv.gov.ua/handle/123456789/181186 004.932 en Control systems & computers Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України
institution Digital Library of Periodicals of National Academy of Sciences of Ukraine
collection DSpace DC
language English
topic Intellectual Informational Technologies and Systems
Intellectual Informational Technologies and Systems
spellingShingle Intellectual Informational Technologies and Systems
Intellectual Informational Technologies and Systems
Stelmakh, O.P.
Stetsenko, I.V.
Velyhotskyi, D.V.
Information Technology of Video Data Processing for Traffic Intensity Monitoring
Control systems & computers
description Traffic jams are a huge problem for all road users and are caused by increasing traffic intensity and poor quality of traffic management systems. The systems that control traffic flows and decide to change 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 method of obtaining the traffic congestion coefficient (TLCR) according to the data, gained 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 new in formation 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 of99,35 percent.
format Article
author Stelmakh, O.P.
Stetsenko, I.V.
Velyhotskyi, D.V.
author_facet Stelmakh, O.P.
Stetsenko, I.V.
Velyhotskyi, D.V.
author_sort Stelmakh, O.P.
title Information Technology of Video Data Processing for Traffic Intensity Monitoring
title_short Information Technology of Video Data Processing for Traffic Intensity Monitoring
title_full Information Technology of Video Data Processing for Traffic Intensity Monitoring
title_fullStr Information Technology of Video Data Processing for Traffic Intensity Monitoring
title_full_unstemmed Information Technology of Video Data Processing for Traffic Intensity Monitoring
title_sort information technology of video data processing for traffic intensity monitoring
publisher Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України
publishDate 2020
topic_facet Intellectual Informational Technologies and Systems
url http://dspace.nbuv.gov.ua/handle/123456789/181186
citation_txt Information Technology of Video Data Processing for Traffic Intensity Monitoring / O.P. Stelmakh, I.V. Stetsenko, D.V. Velyhotskyi // Control systems & computers. — 2020. — № 3. — С. 50-59. — Бібліогр.: 16 назв. — англ.
series Control systems & computers
work_keys_str_mv AT stelmakhop informationtechnologyofvideodataprocessingfortrafficintensitymonitoring
AT stetsenkoiv informationtechnologyofvideodataprocessingfortrafficintensitymonitoring
AT velyhotskyidv informationtechnologyofvideodataprocessingfortrafficintensitymonitoring
first_indexed 2023-10-18T22:51:49Z
last_indexed 2023-10-18T22:51:49Z
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