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

Abstract

Pedestrian lane detection is one of the most crucial tasks in assistive navigation for vision-impaired people. It can provide information on walkable regions, help blind people stay on pedestrian lanes, and assist with obstacle detection. An accurate and real-time lane detection algorithm can improve travel safety and efficiency for the visually impaired. However, despite being an important task for assistive navigation systems, pedestrian lane detection in unstructured scenes has not attracted sufficient attention in the research community. Hence, 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 researchers in this area. This study includes methods proposed for pedestrian lane detection, general road detection, and general semantic segmentation. We review these methods in two categories: traditional methods and deep learning methods. We perform an experimental evaluation of representative methods on a large benchmark dataset specifically created for pedestrian lane detection. We hope this paper can serve as an informative guide for researchers in the field of assistive navigation, and facilitate urgently-needed research for blind people.