Real-Time Detection and Classification of Road Lane Markings

This paper presents a method for detection and recognition of road lane markings using an uncalibrated onboard camera. Initially, lane boundaries are detected based on a linear parabolic model. Then, we build a simple model to represent pixels related to the pavement, and explore this model to estimate pixels related to lane markings. A set of features is computed based on the detected lane markings, and a cascade of binary classifiers is adopted to distinguish five types of markings: dashed, dashed-solid, solid-dashed, single-solid and double-solid. Experimental results show that the proposed method presents good classification results under a variety of situations (shadows, varying illumination, etc.).