Color-based road detection in urban traffic scenes
Color-based road detection in urban traffic scenes
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DOI：
10.1109/tits.2004.838221
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发表时间：
2004-12-01
影响因子：
8.5
通讯作者：
Zhang, B
中科院分区：
文献类型：
--
作者：
He, YH;Wang, H;Zhang, B
道路检测是城市交通中自动驾驶的关键问题。本文在对现有的道路区域检测方法进行简要综述后，提出了一种基于彩色图像的道路区域检测算法。该算法由两个模块组成：首先基于亮度图像进行边界估计，然后基于全色图像进行道路区域检测。在第一模块中，分析场景的边缘图像以获得左右道路边界的候选者，并界定随后将用于计算高斯分布的均值和方差的区域，假设道路表面的颜色分量服从高斯分布。第二个模块有效地提取道路区域，并加强最适合道路提取结果的边界。这些模块的组合可以克服由于单独基于强度图像的边缘检测不准确以及基于彩色图像的分割算法的计算复杂性而导致的基本问题。在真实的道路场景上的实验结果证明了该方法的有效性。
Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.