A self-adaptive Gaussian mixture model
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-17-1842
- Type
- D - Journal article
- DOI
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10.1016/j.cviu.2014.01.004
- Title of journal
- Computer Vision and Image Understanding
- Article number
- -
- First page
- 35
- Volume
- 122
- Issue
- -
- ISSN
- 1077-3142
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- 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
-
-
- Research group(s)
-
-
- Citation count
- 63
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes an algorithm to detect objects in outdoor environments, where changes in illumination hamper effective detection. The significance of this research is linked to the increasing deployment and reliance on video cameras to provide remote monitoring and surveillance, particularly in man-made environments, where robust detection underpins the effectiveness of subsequent video analysis steps.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -