Step Detection for Walking Support of Visually Impaired Using Depth Images

Currently, the number of visually impaired people is on the rise. There are many accidents involving the visually impaired while walking outdoors, and demand for walking support systems is high. In recent years, the number of stations with platform doors on the platform has been increasing, but there are still many stations that do not have such doors, which may cause accidents involving falls. In addition, visually impaired people cannot tell whether the steps in front of them are stairs or dangerous steps such as platforms. In this paper, we propose a system to detect upward and downward steps by combining depth images and CNN models. In particular, for downstairs steps, we distinguish between progressible steps such as stairs and dangerous steps such as platforms. In this research, we take depth images using a stereo camera that can obtain depth images, and create a data set. Using that dataset and CNNs, we will build a model that classifies four classes: “down_stairs”, “downstep”, “up_step”, and “plane”.