Pedestrian Lane Detection for Assistive Navigation of Vision-Impaired People: Survey and Experimental Evaluation

Abstract

Pedestrian lane detection is a crucial task in assistive navigation for vision-impaired people. It can provide information on walkable regions, help blind people stay on the pedestrian lane, and assist with obstacle detection. An accurate and real-time lane detection algorithm can improve travel safety and efficiency for the visually impaired. Despite its importance, pedestrian lane detection in unstructured scenes for assistive navigation has not attracted sufficient attention in the research community. This paper aims to provide a comprehensive review and an experimental evaluation of methods that can be applied for pedestrian lane detection, thereby laying a foundation for future research in this area. Our study covers traditional and deep learning methods for pedestrian lane detection, general road detection, and general semantic segmentation. We also perform an experimental evaluation of the representative methods on a large benchmark dataset that is specifically created for pedestrian lane detection. We hope this paper can serve as an informative guide for researchers in assistive technologies, and facilitate urgently-needed research for vision-impaired people.

Keywords

• Pedestrian lane detection
• assistive navigation
• vision impairment
• semantic segmentation
• deep networks