@inproceedings{9bebdea7cc6743429a4754cb1d9f31a5,
title = "Lane detection based on guided RANSAC",
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.",
keywords = "Computer vision, Driving assistance, Lane detection, RANSAC",
author = "Yi Hu and Kim, {You Sun} and Lee, {Kwang Wook} and Ko, {Sung Jea}",
year = "2010",
language = "English",
isbn = "9789896740283",
series = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",
pages = "457--460",
booktitle = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",
note = "5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 ; Conference date: 17-05-2010 Through 21-05-2010",
}