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

MTU Project Information


NuRail Project IDNURail2020-MTU-R19
Project TitleComputer Learning and AI-Based Investigation of Outward Facing Locomotive Videos for Trespassing Events and Behavior
UniversityMichigan Technological University
Project ManagerPasi Lautala
Principal InvestigatorTimothy C. Havens
PI Contact Informationptlautal@mtu.edu
Funding Source(s) and Amounts Provided (by each agency or organization)$9,000 NURail, $25,000 Michigan Tech
Total Project Cost$34,000
Agency ID or Contract Number
Start Date2020-01-01
End Date2020-08-31
Location
Brief Description of Research ProjectThis 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. Our 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. Our 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.
Describe Implementation of Research Outcomes (or why not implemented)
Photos
Impacts/Benefits of Implementation (actual, not anticipated)
Web Linkshttp://;
Reports
Project Website
Completedyes
Final ReportNURail2020-MTU-R19_Final-Report-Locomotive-Cameras.pdf