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U.S. Department of Transportation U.S. Department of Transportation Icon United States Department of Transportation United States Department of Transportation

Railroad Trespass Detection Using Deep Learning-Based Computer Vision

Document Series
Research Results
Author
Federal Railroad Administration
Report Number
RR 22-04
Office
RPD-33
Subject Grade Crossing Modeling and Simulation, Grade Crossing Technology, Highway-Rail Grade Crossings
Keywords
Automated railroad trespass detection, artificial intelligence, AI, machine learning, railroad trespassing, grade crossing violations, railroad safety

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. 


Last updated: Tuesday, February 8, 2022