Automated road pavement marking detection from high resolution aerial images based on multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remotely sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: (i) road centerline extraction from low resolution image, (ii) road surface detection in the original image, and (iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

I. Introduction

Nowadays there is a growing need for accurate road models for various applications e.g. traffic monitoring, city planning and autonomous navigation. Especially the advanced vehicle assistance system (ADAS), includes applications such as lane departure warning, lane-level vehicle navigation, asks for not only the directional information but also the accurate road lane marking details, e.g. the number of lanes, the location and type of lane markings.