Lane detection based on guided RANSAC

Yi Hu, You Sun Kim, Kwang Wook Lee, Sung Jea Ko

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, a robust and real-time lane detection method is proposed. The method consists of two steps, the lane-marking detection and lane model fitting. After detecting the lane marking by the Intensity bump algorithm, we apply the post filters by constraining the parallelism of lane boundary. Then, a novel model fitting algorithm called Guided RANSAC is presented. The Guided RANSAC searches lanes from initial lane segments and the extrapolation of lane segments is used as the guiding information to elongate lane segments recursively. With the proposed method, the accuracy of the model fitting is greatly increased while the computational cost is reduced. Both theoretical and experimental analysis results are given to show the efficiency.

    Original languageEnglish
    Title of host publicationVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
    Pages457-460
    Number of pages4
    Publication statusPublished - 2010
    Event5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 - Angers, France
    Duration: 2010 May 172010 May 21

    Publication series

    NameVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
    Volume1

    Other

    Other5th International Conference on Computer Vision Theory and Applications, VISAPP 2010
    Country/TerritoryFrance
    CityAngers
    Period10/5/1710/5/21

    Keywords

    • Computer vision
    • Driving assistance
    • Lane detection
    • RANSAC

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Science Applications
    • Computer Vision and Pattern Recognition

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