Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model
Robust lane detection and tracking approach using improved Hough transform and Gaussian Mixture Model is proposed in this paper. The approach consists of three parts: lane markings detection, lane parameters estimation and lane position tracking. Firstly, lane marking pixels are extracted using edge and color features. Then, these pixels are used to estimate the lane boundaries. After the vanishing point has been predicted by a RANSAC algorithm, we use an improved Hough transform to detect the straight lane boundaries in the near field, and apply a parabolic model to represent curved lanes probably existed in the far field. Finally, a novel lane parameters determination method, which uses Gaussian Mixture Model to represent and update the parameters of lane boundaries, is proposed to ensure the stability of the lane tracking system. The proposed approach is tested with some real videos captured on a highway with challenging road environments, and the results demonstrate that our system is very reliable and can also be implemented in real-time.
lane detection lane tracking Gaussian Mixture Model improved Hough transform RANSAC
Yun Zhang Junbin Gong Jinwen Tian
State Key Laboratory for Multi-spectral Information Processing Technology, Institute for PatternReco China Ship Design and Research Center, Wuhan 430064, China
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）