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.

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    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
    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:
    Depositing User: Jing Wang
    Date Deposited: 26 Mar 2018 11:35
    Last Modified: 27 Mar 2018 22:21

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