Skip to Content
No preview available

Actions

Download Analytics Citations

Export to: EndNote  |  Zotero  |  Mendeley

Collections

This file is not currently in any collections.

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).

Descriptions

Resource type
Dataset
Contributors
Creator: Breckon, Toby P. 1
Contact person: Breckon, Toby P. 1
Creator: Webster, Dereck D. 2
1 Durham University, UK
2 Cranfield University, UK
Funder
Higher Education Funding Council for England
Research methods
Other description
Keyword
aindrop detection, rain detection, rain removal, rain noise removal, rain interference, scene context, raindrop saliency, rain classification
image processing
computer vision
Subject
Computer Science
Engineering
Location
UK
Language
English
Cited in
10.1109/ICIP.2015.7351633
Identifier
doi:10.15128/4f16c2806
ark:/32150/4f16c2806
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
September 2015

File Details

Depositor
T. Breckon
Date Uploaded
Date Modified
17 May 2016, 14:05:24
Audit Status
Audits have not yet been run on this file.
Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 23254908
Last modified: 2016:01:21 18:50:25+00:00
Filename: webster15raindrops-dataset.zip
Original checksum: f731d83133dbaf49ac4135fb01990f01
Activity of users you follow
User Activity Date
User S. Palucha has updated Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation - Evaluation Dataset about 3 years ago
User S. Palucha has updated Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation - Evaluation Dataset about 3 years ago