Learning a discriminative null space for person re-identification
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 456
- Type
- E - Conference contribution
- DOI
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10.1109/CVPR.2016.139
- Title of conference / published proceedings
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- First page
- 1239
- Volume
- 2016-December
- Issue
- -
- ISSN
- 1063-6919
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- 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
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2
- Research group(s)
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-
- Citation count
- 294
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Funded by EU Security Programme SUNNY (Smart UNmanned aerial vehicle sensor Network for detection of border crossing and illegal entrY). An influential landmark work in the Person Re-Identification research community for solving the fundamental Small Sample Size (SSS) learning problem in Re-ID, widely adopted and cited by both academic and industrial labs as a benchmark for comparative evaluations of distance metric learning algorithms and systems. Laid the foundation for an Invited International Journal of Computer Vision paper in 2018. Contributed to Gong?s IET 2020 Achievement Award.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -