Ibarra-Arenado Manuel José, Tjahjadi Tardi, Pérez-Oria Juan
Department of Electrical and Energy Engineering, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain.
School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK.
Sensors (Basel). 2020 Feb 13;20(4):1012. doi: 10.3390/s20041012.
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.
基于视觉的驾驶员辅助系统中一个众所周知的挑战是道路上的投射阴影，这使得道路和车道检测等基本任务变得困难。由于阴影检测依赖于阴影特征，在本文中，我们基于天空光和阳光对路面色度的贡献，提出了一组新的阴影色度特性。从这些特性中得出了对阴影区域和非阴影区域的六个约束条件。这些色度特性和相关约束条件被用作一种有效的阴影检测方法中的阴影特征，该方法旨在集成到车载道路检测系统中，在该系统中，识别道路上的投射阴影是一个决定性阶段。车载系统处理的是静止的室外图像；因此，该方法通过考虑天空和太阳这两个照明源，专注于将阴影边界与材质变化区分开来。非阴影道路区域由天空光和阳光共同照明，而阴影区域仅由天空光照明；因此，它们的色度会有所不同。阴影边缘检测策略包括识别分隔阴影道路区域和非阴影道路区域的图像边缘。通过验证图像边缘两侧区域的像素色度值是否满足这六个约束条件来实现分类。在真实交通场景上的实验证明了我们的阴影检测系统在检测道路上的阴影边缘和材质变化边缘方面的有效性，优于以往基于物理特征的阴影检测方法，并显示了新色度特性的巨大潜力。
