Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model

Yun Zhang; Junbin Gong; Jinwen Tian

doi:10.1117/12.901632

8 December 2011 Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model

Yun Zhang, Junbin Gong, Jinwen Tian

Proceedings Volume 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis; 80030O (2011) https://doi.org/10.1117/12.901632
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

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.

Citation Download Citation

Yun Zhang, Junbin Gong, and Jinwen Tian "Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model", Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 80030O (8 December 2011); https://doi.org/10.1117/12.901632

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