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Title:

Artificial Intelligence-Aided Automated Detection of Railroad Trespassing

Accession Number:

01704852

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This framework was implemented on three railroad video live streams, a grade crossing and two right-of-ways, in the United States. The AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections yet. This paper and its subsequent studies aim to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of an existing closed-circuit television infrastructure through the real-time analysis of their data feeds. The data generated from these studies will potentially help researchers understand human factors in railroad safety research and give them a real-time edge on tackling the critical challenges of trespassing in the railroad industry.

Report/Paper Numbers:

19-05004

Language:

English

Authors:

Zaman, Asim
Ren, Baozhang
Liu, Xiang

Pagination:

pp 25-37

Publication Date:

2019-7

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2673
Issue Number: 7
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Digital/other

Features:

Figures (11) ; References (47)

Subject Areas:

Pedestrians and Bicyclists; Railroads; Safety and Human Factors

Files:

TRIS, TRB, ATRI

Created Date:

Apr 8 2019 3:17PM