A Fast Algorithm Based on RANSAC for Vision Lane Detection

Abstract

Abstract:

Abstract： In view of the problems that the real-time, robustness and efficient of the existing lane detection algorithm are low, an improved and fast vision lane detection algorithm based on RANSAC (random sample consensus) was proposed. First, the inverse perspective mapping was conducted. Then, the image was filtered using anisotropic Gasssian filters. The quantile threshold method which has a strong adaptability to different illumination brightness image was used to the filtered image. The initial lines were detected using the histogram statistics method because almost all the lanes in the transform image were vertical. After that, an improved and fast RANSAC curve fitting step was performed to refine the detected initial lines and correctly detect curved lanes. Finally, a postprocessing was conducted to further improve the accuracy of algorithm. The results show that the improved algorithm has a great robustness, strong stability and high efficiency, which can meet the requirements of intelligent vehicle realtime detection.
Key words：

Key words: intelligent transportation, road lane detection, random sample consensus （RANSAC）, Bezier spline

PENG Hong1,XIAO Jinsheng1,2，SHEN Sanming3,LI Bijun2,CHEN Xian1. A Fast Algorithm Based on RANSAC for Vision Lane Detection[J]. Journal of Shanghai Jiaotong University, 2014, 48(12): 1721-1726.

References

［1］Aharon Bar Hillel, Ronen Lerner, Dan Levi, et al. Recent progress in road and lane detection: A survey ［J］. Machine Vision and Applications, 2014,25:727745.

［2］Lee J W, Yi U K. A lanedeparture identification based on LBPE, Hough transform, and linear regression［J］. Computer Vision and Image Understanding, 2005,99(3):359383.

［3］Kang Y, Yamaguchi K, Naito T, et al. Multiband image segmentation and object recognition for understanding road scenes ［J］. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4):14231433.

［4］许华荣, 王晓栋, 方遒. 基于B样条曲线模型的结构化道路检测算法［J］. 自动化学报, 2011,37(3):270275.

XU Huarong,WANG Xiaodong,FANG Qiu. Structure road detection algorithm based on Bspline curve mode［J］. Acta Automatica Sinica, 2011,37(3):270275.

［5］Park J W, Lee J W, Jhang K Y. A lanecurve detection based on an LCF［J］. Pattern Recognition Letters, 2003, 24(14): 23012313.

［6］Zhao K, Meuter M, Nunn C, et al. A novel multilane detection and tracking system ［C］//IEEE Intelligent Vehicles Symposium. Alcala de Henares:IEEE,2012: 10841089

［7］Truong Q B, Lee B R, Heo N G, et al.Lane boundaries detection algorithm using vector lane concept［C］//Control Automation Robotics & Vision. Hanoi: IEEE, 2008: 23192325.

［8］XU Huarong, WANG Xiaodong, HUANG Hongwu, et al. A fast and stable lane detection method based on Bspline curve［C］//IEEE ComputerAided Industrial Design & Conceptual Design. Wenzhou:IEEE, 2009:10361040.

［9］Watanabe A, Naito T, Ninomiya Y. Lane detection with roadside structure using onboard monocular camera ［C］//IEEE Intelligent Vehicles Symposium. Xi’an: IEEE, 2009: 191196.

［10］Hofmann U, Rieder A, Dickmanns E D. EMSvision: Application to hybrid adaptive cruise control［C］//Proceedings of the IEEE Intelligent Vehicles Symposium. Dearborn: IEEE, 2000: 468473.

［11］Lee J W, Kee C D, Yi U K. A new approach for lane departure identification［C］//Intelligent Vehicles Symposium. Parma Italy: IEEE, 2003: 100105.

［12］Jung C R, Kelber C R. A lane departure warning system based on a linearparabolic lane model［C］//Intelligent Vehicles Symposium. Parma Italy: IEEE, 2004: 891895.

［13］Aly M. Real time detection of lane markers in urban streets ［C］//IEEE Intelligent Vehicles Symposium. Eindhoven: IEEE, 2008: 712.

［14］陈龙,李清泉,毛庆洲.基于成像模型的车道线检测与跟踪方法［J］.中国公路学报, 2011,24(6):96102.

CHEN Long, LI Qingquan,MAO Qingzhou.Lane detection and following algorithm based on image model［J］. China Joural of Highway and Transport, 2011,24(6):96102.

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