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Improved raindrop detection using combined shape and saliency descriptors with scene context isolation.

Webster, D.D. and Breckon, T.P. (2015) 'Improved raindrop detection using combined shape and saliency descriptors with scene context isolation.', in 2015 IEEE International Conference on Image Processing, ICIP 2015, 27-30 September 2015, Quebec City, QC, Canada ; proceedings. New York, USA: IEEE, pp. 4376-4380.


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.

Item Type:Book chapter
Keywords:Rain detection, Raindrop distortion, All-weather computer vision, Automotive vision.
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:13 October 2015
Date of first online publication:September 2015
Date first made open access:No date available

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