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This article is cited in 7 scientific papers (total in 7 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
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
Abstract:
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.
Keywords:
integral optical flow, image processing, road traffic control, video surveillance.
Received: 14.01.2019 Accepted: 18.04.2019
Citation:
H. Chen, Sh. Ye, A. Nedzvedz, O. Nedzvedz, H. Lv, S. V. Ablameyko, “Traffic extreme situations detection in video sequences based on integral optical flow”, Computer Optics, 43:4 (2019), 647–652
Linking options:
https://www.mathnet.ru/eng/co688 https://www.mathnet.ru/eng/co/v43/i4/p647
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Abstract page: | 134 | Full-text PDF : | 31 | References: | 17 |
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