Weakly supervised activity analysis with spatio-temporal localisation
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-53-1382
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2016.08.032
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 778
- Volume
- 216
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- -
- 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
-
-
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduced a novel weakly supervised learning framework to analyse human activities taking advantage of weakly annotated video data. This research is one of the first examples of machine learning introduced for assisted living applications. This work is significant because it contributes to facilitate the deployment of video analytics to assist the elderly in a cluttered environment, such as a nursing home. Although controversial, such approach is seen as essential in our aging societies to offer the best affordable care. Indeed, it offers a view of unfolding and potentially hazardous situation, providing a means to minimise accidents.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -