| URBAN VISION DEVELOPMENT IN ORDER TO MONITOR WHEELCHAIR USERS BASED ON THE YOLO ALGORITHM |
| Paper ID : 1205-SMPR |
| Authors: |
|
Ali Ahmadi *1, Meysam Argany2, Najmeh Neysani Samany3, Mahmood Rasooli4 1Remote sensing & GIS department, faculty of Geography, university of Tehran, Tehran, Iran 2Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran, Iran 3Remote sensing & GIS, faculty of Geography, university of Tehran, Tehran, Iran 4Department of software engineering, Shahab Danesh university, Qom, Iran |
| 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 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 canter in Quebec, Canada. The results show that the proportion of wheelchair users in the sample community is 11.83%, which is acceptable in comparison with the population with disabilities in the province of Quebec (12.75%). |
| Keywords: |
| Wheelchair Detection, Urban Vision, Artificial Intelligence, YOLOv3, Disability |
| Status : Conditional Accept (Oral Presentation) |
