Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 25–27, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-25-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 25–27, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-25-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

URBAN VISION DEVELOPMENT IN ORDER TO MONITOR WHEELCHAIR USERS BASED ON THE YOLO ALGORITHM

A. Ahmadi1, M. Argany1, N. Neysani Samany1, and M. Rasooli2 A. Ahmadi et al.
  • 1Geography Faculty, University of Tehran, Tehran, Iran
  • 2Department of software engineering, Shahab Danesh university, Qom, Iran

Keywords: Wheelchair Detection, Urban Vision, Artificial Intelligence, YOLOv3, Disability

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%.