Cloud-based scalable object detection and classification in video streams
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15
- Type
- D - Journal article
- DOI
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10.1016/j.future.2017.02.003
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 286
- Volume
- 80
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Technical exception
- Month of publication
- February
- Year of publication
- 2017
- 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)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This substantial FGCS journal paper's novel cloud-based GPU approach to automating videostream analysis from standard camera technology has made significant impact, underpinning four successful KTP grants:
Anjum and Manning’s ‘Automated digital twinning’, Bloc Graphics Ltd. [https://info.ktponline.org.uk/action/details/partnership.aspx?id=11936].
Anjum and Reiff-Marganiec’s ‘Automated augmented reality construction’, Bloc Graphics Ltd. [https://info.ktponline.org.uk/action/details/partnership.aspx?id=11251].
Anjum and Reiff-Marganiec’s `Automated real-time object tracking solution’, RDS Global Ltd. [https://info.ktponline.org.uk/action/details/partnership.aspx?id=11670].
Anjum and Lu’s ‘Automated semantic data parser from real-time video for augmented reality’ Bloc Graphics Ltd. [https://info.ktponline.org.uk/action/details/partnership.aspx?id=12293]"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -