Video analytics for the detection of near-miss incidents on approach to railway level crossings

Aminmansour, Sina, Maire, Frederic, & Wullems, Christian (2014) Video analytics for the detection of near-miss incidents on approach to railway level crossings. In Proceedings of 2014 Joint Rail Conference, American Society of Mechanical Engineering, Colorado Springs, CO, USA.

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Abstract

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 causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events.

The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle.

Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis.

We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

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ID Code: 67006
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Computer Vision, Image Processing, Rail Safety, Video Analytics
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
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: Copyright 2014 American Society of Mechanical Engineering
Deposited On: 07 Feb 2014 02:11
Last Modified: 10 May 2015 07:41

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