RadarCat : Radar Categorization for input & interaction
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 252008730
- Type
- E - Conference contribution
- DOI
-
10.1145/2984511.2984515
- Title of conference / published proceedings
- Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16)
- First page
- 833
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- October
- Year of publication
- 2016
- 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)
-
E - Human-Computer Interaction
- Citation count
- 40
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Object classification has many applications in optical image analysis. However, the sensor cannot classify without a direct line-of-sight. This paper classifies objects using miniaturised radar and machine learning, allowing classification of objects behind opaque materials. It has created many new research and industrial projects, including: a Home Office-funded project to develop a threat detection system; Cubic Transportation Systems plan to use the technology in transport turnstiles; and Beko plans to use the technology in a novel smart fridge.
Senior Specialist
BEKO PLC R&D Centre
Senior Research & Development Engineer
Cubic Transportation Systems Ltd
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
