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Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation

Webster, D.D.; Breckon, T.P.


D.D. Webster


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.


Webster, D., & Breckon, T. (2015). Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation. In Proc. Int. Conf. on Image Processing (4376-4380).

Conference Name Proceedings of IEEE International Conference on Image Processing
Conference Location Québec City, Canada
Start Date Sep 27, 2015
End Date Sep 30, 2015
Publication Date 2015
Deposit Date Oct 4, 2015
Publicly Available Date Oct 13, 2015
Publisher Institute of Electrical and Electronics Engineers
Pages 4376-4380
Book Title Proc. Int. Conf. on Image Processing
Keywords raindrop detection, rain detection, rain removal, rain noise removal, rain interference, scene context, raindrop saliency, rain classification
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