Unsupervised human activity analysis for intelligent mobile robots
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-158
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2018.12.005
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 67
- Volume
- 270
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- January
- 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
-
2
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presents novel methods for learning about activities in an unsupervised way, which greatly increases its potential significance over most work which requires manual supervision. Extended version of runner-up best student paper (ECAI-16) and which was invited for submission to the AI journal (still with regular peer review). A video exploiting the work was awarded Best Video at IJCAI-17. Cohn and Hogg have both been invited to give keynote/plenary talks based on the work (e.g. RITA-2017, SmartWorld-2019, ICNIS-2019, ACS-17, L2A2-19, Cognitum-17, R2K-18). The first author’s PhD was based on this work and contributed to his obtaining a postdoc at Oxford University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
