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Method of Automated Detection of Traffic Violation with a Convolutional Neural Network

EPJ Web of Conferences, 2019, Vol.224, p.4004 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2100-014X ;ISSN: 2101-6275 ;EISSN: 2100-014X ;DOI: 10.1051/epjconf/201922404004

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  • Title:
    Method of Automated Detection of Traffic Violation with a Convolutional Neural Network
  • Author: Ibadov, S.R. ; Kalmykov, B.Y. ; Ibadov, R.R. ; Sizyakin, R.A.
  • Pivkin, P. ; Egiazarian, K. ; Jiang, X. ; Aleksic, N. ; Uvarova, L. ; Nadykto, A. ; Zelensky, A. ; Lima, P.
  • Subjects: Artificial neural networks ; Motor vehicles ; Neural networks ; Object recognition ; Pedestrian crossings ; Pedestrians
  • Is Part Of: EPJ Web of Conferences, 2019, Vol.224, p.4004
  • Description: 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.
  • Publisher: Les Ulis: EDP Sciences
  • Language: English
  • Identifier: ISSN: 2100-014X
    ISSN: 2101-6275
    EISSN: 2100-014X
    DOI: 10.1051/epjconf/201922404004
  • Source: EDP Open
    GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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