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Crowd Anomaly Detection for Automated Video Surveillance

Wang, Jing and Xu, Zhijie (2015) Crowd Anomaly Detection for Automated Video Surveillance. In: 6th International Conference on Imaging for Crime Prevention and Detection. ICDP (15). IET, London, UK, p. 4. ISBN 978-1-78561-131-5

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Abstract

Video-based crowd behaviour detection aims at tackling challenging problems such as automating and identifying changing crowd behaviours under complex real life situations. In this paper, real-time crowd anomaly detection algorithms have been investigated. Based on the spatio-temporal video volume concept, an innovative spatio-temporal texture model has been proposed in this research for its rich crowd pattern characteristics. Through extracting and integrating those crowd textures from surveillance recordings, a redundancy wavelet transformation-based feature space can be deployed for behavioural template matching. Experiment shows that the abnormality appearing in crowd scenes can be identified in a real-time fashion by the devised method. This new approach is envisaged to facilitate a wide spectrum of crowd analysis applications through automating current Closed-Circuit Television (CCTV)-based surveillance systems.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
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Depositing User: Jing Wang
Date Deposited: 11 Jun 2015 15:44
Last Modified: 01 Dec 2016 23:36
URI: http://eprints.hud.ac.uk/id/eprint/24793

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