Computer Learning and AI-Based Investigation of Outward Facing Locomotive Videos for Trespassing Events and Behavior

This project attempts to use analysis of big data through artificial intelligence (AI) and computer learning (CL) to better understand the leading causes for trespassing incidents, which account for approximately 70% of all railroad-related deaths in the United States. However, it is unknown how many risky events takes place for each incident/casualty. The main data set for the analysis is the video feed from outward facing cameras located in locomotives. Such data are regularly used for analyzing the events in the case of trespasser fatality or serious accident, but when combined with proper analytics and technology, offer an opportunity to identify all trespassing events, not only those reported or those leading to casualties, and then use that enlarged understanding toward more systematic analysis of trespasser events, both from spatial and behavioral perspectives. The approach is to first develop an automatic “trigger” algorithm when human movement is identified in the outward facing locomotive camera and then, in the long-term, use the video data before and after the trigger event to 1) locate every trespasser event visible from the video (within defined limits) and 2) investigate, locational and behavioral trends, including causal factors through application of artificial intelligence, computer learning, and human models on trespasser events.


    • Status: Active
    • Contract Numbers:



    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Lautala, Pasi

    • Start Date: 20200101
    • Expected Completion Date: 20200831
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01736122
    • Record Type: Research project
    • Source Agency: National University Rail Center (NURail)
    • Contract Numbers: DTRT13-G-UTC52, NURail2020-MTU-R19
    • Files: UTC, RiP
    • Created Date: Apr 13 2020 3:15PM