A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation
- Submitting institution
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Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 187378502
- Type
- D - Journal article
- DOI
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10.3390/s19143197
- Title of journal
- Sensors (Basel, Switzerland)
- Article number
- 3197
- First page
- -
- Volume
- 19
- Issue
- 14
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- 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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4
- Research group(s)
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B - Civil and Construction
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is an output of a USA-Ireland tripartite research project funded by the NSF(USA)/SFI(Ireland)/DfE(NI) [NSF grant no.1463493]. This research, for the first time, overcame the challenge of environmental factors in computer vision monitoring techniques, such as fog and illumination changes, and achieved result sensitivity at the subpixel level by incorporating spatial and temporal contextual aspects. The computer vision techniques developed on this project were deployed in monitoring a major pedestrian bridge which is corroborated by DfI. This has led to new research with Wrightbus in drive-by monitoring toward zero net emissions public transport (EP/S036695/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -