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IPOD Abstract for presentation (Poster or Podium)

Rail Transport

1667008 - Leveraging Computer Vision and Artificial Intelligence for Enhanced Railway Safety

Tuesday, June 18, 2024
10:45 AM - 11:00 AM ET
Room: 215

    Primary Author(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Co-Author(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Presenter(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Co-Author(s)

  • Karen Giese, P.E.

    Vice President, US Business Development
    Transoft Solutions
    Richmond, BC, Canada

    • Presenter(s)

  • Karen Giese, P.E.

    Vice President, US Business Development
    Transoft Solutions
    Richmond, BC, Canada

    • Submitter(s)

  • BJ

    Behnam Jamali

    Transoft Solutions
    Montreal, Quebec, Canada

Abstract: Railway systems, being one of the oldest modes of transportation, can be at the leading edge of technological innovation, with Computer Vision and Artificial Intelligence (AI) playing a crucial role. While this technology can have diverse applications for railway transportation including real-time tracks monitoring, anomaly detection, maintenance needs, and potential hazards, this presentation particularly investigates the applications of these advanced technologies in enhancing the safety and efficiency of railways by monitoring pedestrian and vehicle non-compliant behaviours at railway crossings. It emphasizes the role of AI in automating enforcement procedures such as identifying dangerous behaviours, deployment considerations and potential challenges and obstacles including technological limitations, data privacy issues, and the definition of standardised approach to adopt this technology towards a safer and more efficient railway infrastructure. 

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Understand the video-based road user classification and tracking in railway level crossings, aligning with the vehicle classification standard set by the Federal Highway Administration (FHWA)
  • Define two restricted zones as supported in non-compliant behaviour detection using computer vision technology
  • Define seven restricted time slots as supported in non-compliant behaviour detection using computer vision technology