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Open AccessArticle

Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination

1
Department of Electrical and Energy Engineering, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
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School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
3
Department of Electronic Technology and Automatic Systems, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1012; https://doi.org/10.3390/s20041012 (registering DOI)
Received: 30 December 2019 / Revised: 3 February 2020 / Accepted: 10 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Intelligent Vehicles)
A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties. View Full-Text
Keywords: advanced driving assistance systems; illumination; shadow detection; shadow edge; road detection advanced driving assistance systems; illumination; shadow detection; shadow edge; road detection
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Ibarra-Arenado, M.J.; Tjahjadi, T.; Pérez-Oria, J. Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination. Sensors 2020, 20, 1012.

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