IEEE Trans Image Process. 2012 Jan;21(1):425-30. doi: 10.1109/TIP.2011.2162422. Epub 2011 Jul 18.
Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.
基于视觉的非结构化环境中的道路检测是一个具有挑战性的问题，因为在这种环境中几乎没有任何可识别和不变的特征可以描述道路或其边界。然而，大多数道路或轨道的一个显著且一致的特征是，它们的边缘、边界，甚至是先前车辆在路径上留下的车辙和轮胎痕迹，似乎都汇聚到一个称为消失点的单一点上。因此，估计这个消失点在确定道路的方向方面起着至关重要的作用。在本文中，我们提出了一种基于图像纹理分析的新方法，用于快速估计具有挑战性和非结构化道路的消失点。该方法的关键属性包括最优局部主导方向方法，该方法仅使用四个 Gabor 滤波器的联合活动来精确估计图像平面上每个像素位置的局部主导方向，根据像素的主导方向对每个像素进行加权，以及用于估计消失点的自适应基于距离的投票方案。使用国防高级研究计划局大挑战项目中的自然数据集进行了一系列定量和定性分析，以证明所提出方法的有效性和准确性。
