Near-miss event detection at railway level crossings

Aminmansour, Sina, Maire, Frederic, & Wullems, Christian (2014) Near-miss event detection at railway level crossings. In Wang, Lei, Ogunbona, Philip, & Li, Wanqing (Eds.) Proceedings of International Conference on Digital Image Computing : Techniques and Applications (DICTA 2014), IEEE, Wollongong, NSW, pp. 1-8.

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Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains.

As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage.

In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss.

We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

Impact and interest:

2 citations in Scopus
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ID Code: 77500
Item Type: Conference Paper
Refereed: Yes
Keywords: Computer Vision, Image Processing, Rail Safety, Video Analytics, Rail Track Detection
DOI: 10.1109/DICTA.2014.7008119
ISBN: 978-1-4799-5409-4
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
Copyright Owner: Copyright 2014 IEEE
Deposited On: 09 Oct 2014 23:35
Last Modified: 30 Jan 2015 14:17

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