Improving Near-Miss Event Detection Rate at Railway Level Crossings

Sina Aminmansour; Frederic Maire; Gregoire S Larue; Christian Wullems

doi:10.1109/DICTA.2015.7371273

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Improving Near-Miss Event Detection Rate at Railway Level Crossings

Conference paper

Peer reviewed

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Sina Aminmansour, Frederic Maire, Gregoire S Larue and Christian Wullems

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp.1-8

International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2015 (Adelaide, Australia, 23-Nov-2015–25-Nov-2015)

Institute of Electrical and Electronics Engineers

2015

DOI: https://doi.org/10.1109/DICTA.2015.7371273

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Abstract

Detectors

Feature extraction

Image segmentation

Proposals

Rail transportation

Rails

Vehicles

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 of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Details

Title: Improving Near-Miss Event Detection Rate at Railway Level Crossings
Authors: Sina Aminmansour (Author) - Queensland University of Technology
Frederic Maire (Author) - Queensland University of Technology
Gregoire S Larue (Author) - Queensland University of Technology
Christian Wullems (Author) - Queensland University of Technology
Publication details: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp.1-8
Conference details: International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2015 (Adelaide, Australia, 23-Nov-2015–25-Nov-2015)
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2015
DOI: 10.1109/DICTA.2015.7371273
ISBN: 9781467367950
Organisation Unit: Road Safety Research Collaboration; University of the Sunshine Coast, Queensland; School of Law and Society
Language: English
Record Identifier: 99648952102621
Output Type: Conference paper

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