Entrance Guidance System with an RNN for Visually Impaired People

Abstract

Visually impaired people face a variety of problems when traveling outdoors. For this reason, research on systems to assist the visually impaired in walking safely has been conducted in recent years. Most of them focus on obstacle avoidance and use existing navigation services as they are for guidance. However, such systems cannot provide guidance to pinpoints such as building entrances. Therefore, we propose a method of guiding the visually impaired to building entrances using a recurrent model. Our system uses a recurrent machine learning model to output optimal voice instructions based on the user's walking trajectory and time-series data of entrance locations detected by YOLO. By using time-series data, the system can accurately correct the direction of the entrance even if the entrance is temporarily out of the camera's angle of view. Future work includes consideration of the walkable area and implementation of the system in a server-client format.