Cross-view Discriminative Feature Learning for Person Re-Identification
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 153827501
- Type
- D - Journal article
- DOI
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10.1109/TIP.2018.2851098
- Title of journal
- IEEE Trans. on Image Processing
- Article number
- -
- First page
- 5338
- Volume
- 27
- Issue
- 11
- ISSN
- 1057-7149
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- 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)
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C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper addresses a fundamental problem, i.e. the viewpoint variability in person re-identification across a network of nonoverlapping cameras. The proposed method is the key to achieve the state-of-the-art performance in real-world scenarios. This work has been utilized by the company AnyVision in its various products, e.g., video surveillance (contact: Zheng Lu - steven@anyvision.co).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -