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
CountryFrance
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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  • Cite this

    Hu, Y., Kim, Y. S., Lee, K. W., & Ko, S. J. (2010). Lane detection based on guided RANSAC. In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 457-460). (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 1).