Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city

Nasralla, Moustafa, Rehman, Ikram, Sobnath, Drishty and Paiva, Sara (2019) Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city. In: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, 06 Sept 2019, Salerno, Italy.

[img]
Preview
PDF
workshop_paper (Accepted Version).pdf - Accepted Version

Download (183kB) | Preview

Abstract

Technological research and innovation have advanced at a rapid pace in recent years, and one group hoping to benefit from this, is visually impaired people (VIP). Technology may enable them to find new ways of travelling around smart cities, thus improving their quality of life (QoL). Currently, there are approximately 110 million VIP worldwide, and continuous research is crucial to find innovative solutions to their mobility problems. Recent advances such as the increase in Unmanned Aerial Vehicles (UAVs), smartphones and wearable devices, together with an ever-growing uptake of deep learning, computer vision, the Internet of Things (IoT), and virtual and augmented reality (VR)/(AR), have provided VIP with the hope of having an improved QoL. In particular, indoor and outdoor spaces could be improved with the use of such technologies to make them suitable for VIP. This paper examines use cases both indoors and outdoors and provides recommendations of how deep learning and computer vision-enabled UAVs could be employed in smart cities to improve the QoL for VIP in the coming years.

Item Type: Conference or Workshop Item (Paper)
ISSN: 1865-0929
ISBN: 9783030299309
Identifier: 10.1007/978-3-030-29930-9_9
Page Range: pp. 91-99
Additional Information: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-29930-9_9.
Uncontrolled Keywords: Deep Learning, Computer Vision, UAVs, Drone, Visually Impaired People (VIP), Smart City.
Subjects: Computing
Depositing User: Ikram Rehman
Date Deposited: 15 Jul 2019 07:29
Last Modified: 19 Sep 2019 08:29
URI: http://repository.uwl.ac.uk/id/eprint/6245

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu