Urban Vision Development in Order to Monitor Wheelchair Users Based on the Yolo Algorithm

Abstract

Abstract. Disability has been one of the most important problems of social communities throughout the ages. As population and urbanization have grown dramatically over recent years, this problem has more and more created the gap between people with disabilities and ordinary people in terms of access to resources, social services and social partnerships. Therefore, this study attempts to demonstrate the ratio of presence of wheelchair users in a community compared to the total population of the same community and evaluate their patterns of presence in different conditions, for example, various weather conditions. For this purpose, we used the You Look Only Once version 3 (YOLOv3) algorithm which is a multilayer deep learning object detection tool to analyze and extract wheelchair users from three different sets of images taken by a camera located in an intersection proximate to a rehabilitation center in Quebec, Canada. The results show that the proportion of wheelchair users in the sample community is 7.4%, while the population with mobility disabilities in Canada is 9.6%.

Applying Image Analysis to Build a Lightweight System for Blind Obstacles Detecting of Intelligent Wheelchairs

“…The use of wheelchairs among the elderly and individuals with physical disabilities is increasing. The cited paper [1] provides a statistical test of wheelchair usage. However, due to the deterioration of the elderly's physical functions and diminished perceptual abilities, the barriers on both sides of the wheelchair can pose a number of safety risks when using a wheelchair.…”