Human interaction anticipation by combining deep features and transformed optical flow components
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21377388
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2020.3012557
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 137646
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- 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
-
4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This international collaboration has improved 6% the world-wide state-of-the-art in predicting human interaction. This algorithm has the global potential impact to save lives by anticipating criminal activity, providing smart homes enablers, etc. This research was the seed to attract the funded project “Advanced Training in Health Innovation Knowledge Alliance (ATHIKA)”, which also focused on investigating mechanisms to detect and anticipate falling of elderly.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -