Combining priors, appearance, and context for road detection

Jose M. Álvarez, Antonio M. López, Theo Gevers, Felipe Lumbreras

    Research output: Contribution to journalArticlepeer-review

    106 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number6719504
    Pages (from-to)1168-1178
    Number of pages11
    JournalIEEE Transactions on Intelligent Transportation Systems
    Volume15
    Issue number3
    DOIs
    Publication statusPublished - Jun 2014

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