Combining Priors, Appearance, and Context for Road Detection

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...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2014-06, Vol.15 (3), p.1168-1178
Main Authors: Alvarez, Jose M., Lopez, Antonio M., Gevers, Theo, Lumbreras, Felipe
Format: Article
Language:English
Subjects:
3-D scene layout
Cameras
Global Positioning System
Illuminant invariance
Image color analysis
lane markings
Lighting
road detection
road prior
road scene understanding
Roads
Roads & highways
Shape
vanishing point
Vehicles
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2013.2295427