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Эта публикация цитируется в 8 научных статьях (всего в 8 статьях)
ОБРАБОТКА ИЗОБРАЖЕНИЙ, РАСПОЗНАВАНИЕ ОБРАЗОВ
Traffic extreme situations detection in video sequences based on integral optical flow
H. Chena, Sh. Yea, A. Nedzvedzbc, O. Nedzvedzd, H. Lva, S. V. Ablameykobc a Zhejiang Shuren University, Hangzhou, China
b Belarusian State University, Minsk, Belarus
c United Institute of Informatics Problems of National Academy of Sciences, Minsk, Belarus
d Belarusian State Medical University, Minsk, Belarus
Аннотация:
Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.
Ключевые слова:
integral optical flow, image processing, road traffic control, video surveillance.
Поступила в редакцию: 14.01.2019 Принята в печать: 18.04.2019
Образец цитирования:
H. Chen, Sh. Ye, A. Nedzvedz, O. Nedzvedz, H. Lv, С. В. Абламейко, “Traffic extreme situations detection in video sequences based on integral optical flow”, Компьютерная оптика, 43:4 (2019), 647–652
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/co688 https://www.mathnet.ru/rus/co/v43/i4/p647
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