Path and Floor Detection in Outdoor Environments for Fall Prevention of the Visually Impaired Population

Abstract

According to the world report in 2019 about vision health presented by the World Health Organization (WHO), there are 2.2 billion people in the world with some kind of visual impairment. Furthermore, in a recent National Health Interview Survey report, 25.5 million adult Americans 18 and older reported experiencing vision loss. Of these 25.5 million American adults, 15.3 million women and 10.1 million men report experiencing significant vision loss. As a result, this population is constantly at risk of a fall and its consequences. This paper presents the first module of our fall prevention system for visually impaired people. The module corresponds to a system of floor and path recognition in outdoor environments. At the core of the system is a Mask R-CNN trained with around 40.000 outdoor images. The proposed approach has reached a performance in a combination of training and testing of 92%, using a combined set of indoor and outdoor environment images.