Improving Near-Miss Event Detection Rate at Railway Level Crossings

Aminmansour, Sina, Maire, Frederic, Larue, Gregoire S., & Wullems, Christian (2015) Improving Near-Miss Event Detection Rate at Railway Level Crossings. In Digital Image Computing: Techniques and Applications (DICTA) 2015, 23rd - 25th of November 2015, Adelaide, South Australia, Australia.

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Abstract

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events.

To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Impact and interest:

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ID Code: 87371
Item Type: Conference Paper
Refereed: Yes
Keywords: Computer Vision, Artificial Intelligence, Convolutional Neural Network (CNN), Railway Engineering, Vehicle Detection
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Psychology & Counselling
Copyright Owner: Institute of Electrical and Electronics Engineers (IEEE)
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Deposited On: 09 Sep 2015 02:31
Last Modified: 06 Mar 2016 06:32

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