Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection

WANG, Jing and XU, Zhijie (2016). Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection. Computer Vision and Image Understanding, 144, 177-187.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.cviu.2015.08.010
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    Abstract

    With the rapidly increasing demands from surveillance and security industries, crowd behaviour analysis has become one of the hotly pursued video event detection frontiers within the computer vision arena in recent years. This research has investigated innovative crowd behaviour detection approaches based on statistical crowd features extracted from video footages. In this paper, a new crowd video anomaly detection algorithm has been developed based on analysing the extracted spatio-temporal textures. The algorithm has been designed for real-time applications by deploying low-level statistical features and alleviating complicated machine learning and recognition processes. In the experiments, the system has been proven a valid solution for detecting anomaly behaviours without strong assumptions on the nature of crowds, for example, subjects and density. The developed prototype shows improved adaptability and efficiency against chosen benchmark systems.

    Item Type: Article
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Identification Number: https://doi.org/10.1016/j.cviu.2015.08.010
    Page Range: 177-187
    Depositing User: Jing Wang
    Date Deposited: 08 Mar 2018 16:23
    Last Modified: 09 Mar 2018 08:07
    URI: http://shura.shu.ac.uk/id/eprint/18876

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