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Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation - Evaluation Dataset Open Access
The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly import contexts of visual surveillance and vehicle autonomy. A key part of this problem is robust raindrop detection such that the potential for performance degradation in effected image regions can be identified. Here we address the problem of raindrop detection in colour video imagery using an extended feature descriptor comprising localised shape, saliency and texture information isolated from the overall scene context. This is verified within a bag of visual words feature encoding framework using Support Vector Machine and Random Forest classification to achieve notable 86% detection accuracy with minimal false positives compared to prior work. Our approach is evaluated under a range of environmental conditions typical of all-weather automotive visual sensing applications. This is the evaluation data set used in the work and comprises the images and associated ground truth labels used for the results within the paper (doi: 10.1109/ICIP.2015.7351633).
- Resource type
Breckon, Toby P.
Contact person: Breckon, Toby P. 1
Creator: Webster, Dereck D. 2
1 Durham University, UK
2 Cranfield University, UK
Higher Education Funding Council for England
- Research methods
- Other description
- aindrop detection, rain detection, rain removal, rain noise removal, rain interference, scene context, raindrop saliency, rain classification
- Cited in
- Creative Commons Attribution 4.0 International (CC BY)
- Date Created
- T. Breckon
- Date Uploaded
- 21 January 2016, 18:01:36
- Date Modified
- 17 May 2016, 14:05:24
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