Spatio-temporal texture modelling for real-time crowd anomaly detection
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 61
- Type
- D - Journal article
- DOI
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10.1016/j.cviu.2015.08.010
- Title of journal
- Computer Vision and Image Understanding
- Article number
- -
- First page
- 177
- Volume
- 144
- Issue
- -
- ISSN
- 1077-3142
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, published in a Scimago Q1 journal, presented a novel technique for real-time analysis of statistical features from live video feeds. It resolved a long-term challenge in the surveillance industry for event detection and classification (6 Patents on live indoor-outdoor crowd abnormality detection https://helios.hud.ac.uk/sengdg2/Patent%20Search%20and%20Analysis%20-%20Zhijie%20Xu%20with%20CUIT.pdf). The innovation was rooted in author’s early work in action recognition (https://dl.acm.org/doi/10.1016/j.sigpro.2012.06.009) and the latest achievement in crowd behaviour understanding (Best Paper Award: IARIA-ADVCOMP 2018, https://www.iaria.org/conferences2018/awardsADVCOMP18/advcomp2018_a2.pdf). It had been cited widely with a real-world implementation in Shenzhen City reaching 15 police forces and emergency response bureaus (SZ GJHZ20160301164521358 Report: https://helios.hud.ac.uk/sengdg2/SZ%20GJHZ20160301164521358%20Project%20Report.pdf).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -