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Shadow detection in still road images using chrominance properties of shadows and spectral power distribution of the illumination
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Ibarra-Arenado, Manuel José, Tjahjadi, Tardi and Pérez-Oria, Juan (2020) Shadow detection in still road images using chrominance properties of shadows and spectral power distribution of the illumination. Sensors, 20 (4). 1012. doi:10.3390/s20041012 ISSN 1424-8220.
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WRAP-shadow-detection-still-road-images-using-chrominance-properties-shadows-spectral-power-distribution-illumination-Tjahjadi-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (19Mb) | Preview |
Official URL: http://dx.doi.org/10.3390/s20041012
Abstract
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.
| Item Type: | Journal Article | ||||||
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Automobile driving, Driver assistance systems, Intelligent transportation systems, Computer vision, Remote sensing, Driver assistance systems -- Lighting, Shades and shadows | ||||||
| Journal or Publication Title: | Sensors | ||||||
| Publisher: | MDPI AG | ||||||
| ISSN: | 1424-8220 | ||||||
| Official Date: | 13 February 2020 | ||||||
| Dates: |
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| Volume: | 20 | ||||||
| Number: | 4 | ||||||
| Article Number: | 1012 | ||||||
| DOI: | 10.3390/s20041012 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 4 March 2020 | ||||||
| Date of first compliant Open Access: | 6 March 2020 |
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