Detection of lane markings based on ridgeness and RANSAC

Detection of lane markings based on a camera sensor can be a low cost solution to lane departure warning and lateral control. However, reliable detection is difficult due to cast shadows, vehicles occluding the marks, wear, vehicle motion, etc. The contribution of this paper is twofold. Firstly, we propose to explore another low-level image descriptor, namely, the ridgeness, instead of the gradient magnitude with the aim of getting a more reliable lane marking detection under adverse circumstances. Besides, the proposed measure comes with an associated orientation which is less noisy than the gradient one. Secondly, we have adapted RANSAC, a generic robust estimation method, to fit a parametric model to the image lane lines using both ridgeness and orientation as input data. In short, in this paper a better feature type and a robust fitting method are proposed, which contribute to improve the lane lines detection reliability, and still achieving real-time.