Single and multiple object tracking using a multi-feature joint sparse representation
- Submitting institution
-
Birkbeck College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 179
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2014.2353628
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 816
- Volume
- 37
- Issue
- 4
- ISSN
- 0162-8828
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- URL
-
http://eprints.bbk.ac.uk/id/eprint/13325/
- 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)
-
2 - Experimental Data Science
- Citation count
- 69
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents several innovations in multi-object tracking, including a multi-feature joint sparse representation-based appearance model for more robust tracking and a high-accuracy algorithm for multi-object tracking with occlusion handling. The paper underpinned a successful grant application by Weiming Hufor research funding from the National Natural Science Foundation in China, “Machine Learning-Based Brain Inspired Visual Motion Perception” (Jan., 2021—Dec. 2025).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -