Railroad Trespass Detection Using Deep Learning-Based Computer Vision [Research Results]

Details

• Creators:
  Jacobini, Francesco Bedini ; Ngamdung, Tashi
• Corporate Creators:
  United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology ; John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  • Detection and identification systems
  • Railroad grade crossings
  • Artificial intelligence
  • Machine learning
  • Grade crossing protection systems
  • Railroad safety
  • Automated railroad trespass detection
  • railroad trespassing
  • grade crossing violations
• Publication/ Report Number:
  RR 22-04
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Results
• Contracting Officer:
  Jacobini, Francesco Bedini
• Corporate Publisher:
  United States. Department of Transportation. Federal Railroad Administration
• Abstract:
  The U.S. Department of Transportation John A. Volpe National Transportation Systems Center (Volpe), under the direction of the Federal Railroad Administration (FRA) Office of Research, Development, and Technology, developed an AI software application for automating the detection of grade crossing violations and trespass activities from static camera video feeds. The Grade Crossing Trespass Detection GTCD software application outputs predicted grade crossing violations and right-of-way trespassing as tabular data in MS Excel format along with annotated video files of trespass events. Accurately detecting when a trespass event occurs using standard video input reduces the time needed to collect safety data. Currently, railroads and many state DOTs have a wealth of video data on their systems, but that data is generally only analyzed if there is a documented incident. Automated identification and processing of trespass events from the existing video data may yield significant safety data currently not being analyzed.
• Format:
  PDF
• Funding:
  DTFR5317X00026
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/60606/dot_60606_DS1.pdf
• File Type:
  [PDF - 539.67 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Federal Railroad Administration
• Main Document Checksum:
  urn:sha-512:90c8174bb8c4cf52c2d909260f20caeee7487f12c8d95bb0b164d7b172c99063adeb0c0e5bc7d8737fb572bb3723b83c13eed071673bd0f5a56290e8a9854589