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.
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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 Section |
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Additional Information: | Paper originally presented at the 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), London, UK. 15-1 July 2015. INSPEC Accession Number: 15382040 |
Departments - Does NOT include content added after October 2018: | Faculty of Science, Technology and Arts > Department of Computing |
Identification Number: | https://doi.org/10.1049/ic.2015.0102 |
Depositing User: | Jing Wang |
Date Deposited: | 26 Mar 2018 11:35 |
Last Modified: | 18 Mar 2021 15:20 |
URI: | https://shura.shu.ac.uk/id/eprint/18875 |
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