Dual-hand detection for human-robot interaction by a parallel network based on hand detection and body pose estimation
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14575255
- Type
- D - Journal article
- DOI
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10.1109/TIE.2019.2898624
- Title of journal
- IEEE Transactions on Industrial Electronics
- Article number
- -
- First page
- 9663
- Volume
- 66
- Issue
- 12
- ISSN
- 0278-0046
- Open access status
- Compliant
- Month of publication
- February
- 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
-
3
- Research group(s)
-
B - Computational Intelligence
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first published use of a body skeleton to refine dual-hand tracking performance with the ability to distinguish reliably between the right and left hands. The proposed approach has been successfully applied for the interaction between astronauts and an astronaut assistant robot.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -