Detecting anomalous events at railway level crossings

Nallaivarothayan, Hajananth, Ryan, David, Denman, Simon, Sridharan, Sridha, Fookes, Clinton, & Rakotonirainy, Andry (2013) Detecting anomalous events at railway level crossings. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 227(5), pp. 539-553.

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Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.

Impact and interest:

2 citations in Scopus
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ID Code: 62751
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Computer Vision, Imaging/ Image Processing, Image Analysis, Intelligent Systems, Electronic Engineering, Computer Applications
DOI: 10.1177/0954409713501296
ISSN: 0954-4097
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
Deposited On: 23 Sep 2013 01:46
Last Modified: 26 Jun 2015 03:22

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