Lane-mark detection on top-view domain using directional feature-points tracing algorithm

Abstract

This paper presents a lane-mark detection method which can extract lane-mark positions accurately in a crowded road environment. Many existing lane detection methods rely on the fitting of lane model among a great deal of outlier feature-points, while the proposed method aims at removing those outliers as much as possible at feature extraction stage. To achieve this goal, a multi-channel Haar-like filter is firstly used to get possible lane-mark patterns on a top-view image of the road, and then a directional feature-point tracing algorithm is proposed to get feature-point components, which are further classified as lane-mark or non lane-mark components via geometric properties. Based on the extracted lane-mark components, a tangent circle model is fitted by means of circle template matching. Experimental results show that the proposed method is effective in identifying lane-mark patterns among heavy clutters in crowded road environment.