Open AccessTraffic extreme situations detection in video sequences based on integral optical flow
Author(s) -
H. Chen,
Shiping Ye,
A. Nedzved,
O. V. Nedzvedz,
Hexin Lv,
Sergey Ablameyko
Publication year - 2019
Publication title -
kompʹûternaâ optika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 29
eISSN - 2412-6179
pISSN - 0134-2452
DOI - 10.18287/2412-6179-2019-43-4-647-652
Subject(s) - optical flow , computer science , computer vision , artificial intelligence , divergence (linguistics) , flow (mathematics) , segmentation , traffic flow (computer networking) , integral imaging , pixel , movement (music) , motion (physics) , image (mathematics) , mathematics , acoustics , physics , geometry , philosophy , linguistics , computer security
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.