Combining Priors, Appearance, and Context for Road Detection
Combining Priors, Appearance, and Context for Road Detection
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DOI：
10.1109/tits.2013.2295427
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发表时间：
2014-06-01
影响因子：
8.5
通讯作者：
Lumbreras, Felipe
中科院分区：
文献类型：
--
作者：
Alvarez, Jose M.;Lopez, Antonio M.;Lumbreras, Felipe
检测移动车辆前方的自由路面是计算机视觉不同领域的重要研究课题，如自动驾驶或汽车碰撞预警。目前基于视觉的道路检测方法通常只基于底层特征。此外，它们通常假设有结构的道路、道路同质性和均匀的照明条件，这限制了它们在现实场景中的适用性。本文将道路先验和上下文信息引入到道路检测中。首先，我们提出了一种利用地理信息在线估计道路先验的算法，提供了道路位置的相关初始信息。然后，上下文线索，包括地平线、消失点、车道标记、3-D场景布局和道路几何形状，除了从道路外观派生的低级线索外，还被使用。最后，使用生成模型将这些线索和先验结合起来，从而产生一种道路检测方法，该方法在很大程度上对不同的成像条件、道路类型和场景具有鲁棒性。
Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.