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http://dx.doi.org/10.5573/ieek.2013.50.5.224

Robust Lane Detection Method Under Severe Environment  

Lim, Dong-Hyeog (School of Electrical Engineering, Univ. of Ulsan)
Tran, Trung-Thien (School of Electrical Engineering, Univ. of Ulsan)
Cho, Sang-Bock (School of Electrical Engineering, Univ. of Ulsan)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.5, 2013 , pp. 224-230 More about this Journal
Abstract
Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.
Keywords
iVMD; k-means clustering; RANSAC;
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