Histogram of Fuzzy Local Spatio-Temporal Descriptors for Video Action Recognition
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25209613
- Type
- D - Journal article
- DOI
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10.1109/TII.2019.2957268
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- 8919994
- First page
- 4059
- Volume
- 16
- Issue
- 6
- ISSN
- 1551-3203
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- 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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5
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is first time use of fuzzy logic to manage the uncertainty in local feature descriptor extraction in video clips for activity recognition, which leads to comparable results with the popular but resource intensive deep learning approaches. This work is partly supported by the Royal Academy of Engineering (through Newton Fund) project (£49,950, IAPP1\100077) as the fundamental theory will be utilised in the project for feature extraction using deep learning approaches. The underpinning idea has led to some interesting discussions with Dr Richard Jenson and Neil Mac Parthalain, followed by an invited talk at Aberystwyth University in 2018 (email: rjk@aber.ac.uk).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -