https://doi.org/10.1051/epjconf/201922404004
Method of Automated Detection of Traffic Violation with a Convolutional Neural Network
1
Don State Technical University, Institute of Service and Entrepreneurship, RU-346500, Shakhty, Rostov region, Russia
2
South Federal University, Institute of Radio Systems and Control, RU-347928, Taganrog, Rostov region, Russia
3
Department Telecommunications and Information Processing, Ghent University, B-9000, Ghent, Belgium
Published online: 9 December 2019
This article describes the relevance of developing methods and systems for detection photo-video violations of the Rules of the road. The proposed method includes several steps: 1) detecting of the three classes of objects on a video sequence (pedestrian crossing, a motor vehicle and a human on the pedestrian crossing; 2) tracking the trajectories of the vehicle and the human on the pedestrian crossing; 3) comparing the paths of the pedestrian and the vehicle and determining whether there has been a violation of the Rules of the road for a certain period of time. For real-time object detection, we used neural network YOLO V3.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
