Multi-scale Crowd Feature Detection using Vision Sensing and Statistical Mechanics Principles
- Submitting institution
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Bournemouth University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 331517
- Type
- D - Journal article
- DOI
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10.1007/s00138-020-01075-4
- Title of journal
- Machine Vision and Applications
- Article number
- 0
- First page
- 0
- Volume
- 31
- Issue
- 4
- ISSN
- 0932-8092
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2020
- URL
-
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- 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
- Yes
- Number of additional authors
-
-
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper brings computer vision for the measurements of crowd features, for seed behaviour detection and understanding. The features are at meso-scale structure levels. We understand seed behaviour and how it propagates in space and time. This has raised great interest from our contacts in the security domain sectors. Early validation of our approach was conducted at Aneota FC Stadium, Spain (2013-2018 - http://www.evacuate.eu/ ). A new grant (S4Allcities - 2020-2022 - https://www.s4allcities.eu/project) is furthering our research on seed behaviour propagation with its validation in four urban venues. The paper is one of a series of papers, published since 2015.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -