Refined particle swarm intelligence method for abrupt motion tracking
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-52-1381
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2014.01.003
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 267
- Volume
- 283
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- 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)
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- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research presented in this paper addresses tracking in the presence of abrupt motion by casting tracking as an optimisation problem solved by swarm intelligence. It contributes to the field of video analytics with specific focus on abrupt motion, which is of great interest in physical security and sport analytics applications, where dynamics might be abrupt and unpredictable. In addition, it introduces a new dataset (MAMo), which is of great importance in the field of machine/deep learning since only a few are available although essential to train computational models. This research helped securing the H2020 funded MONICA project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -