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Assistive Technology for the Visually Impaired: Optimizing Frame Rate (Freshness) to Improve the Performance of Real-Time Objects Detection Application

Lall, Gurprit S. and Steponenaite, Aiste (2020) Assistive Technology for the Visually Impaired: Optimizing Frame Rate (Freshness) to Improve the Performance of Real-Time Objects Detection Application. In: Universal Access in Human-Computer Interaction. Applications and Practice. HCII 2020. Lecture Notes in Computer Science . Springer, Cham. ISBN 978-3-030-49107-9. E-ISBN 978-3-030-49108-6. (doi:10.1007/978-3-030-49108-6_34) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:82104)

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https://doi.org/DOI: 10.1007/978-3-030-49108-6_34

Abstract

It has been 100+ years since the world’s first commercial radio station started. This century witnessed several astonishing inventions (e.g. the computer, internet and mobiles) that have shaped the way we work and socialize. With the start of a new decade, it is evident that we are becoming more reliant on these new technologies as the majority of the world population relies on the new technology on a daily basis. As world’s population is becoming reliant on new technologies and we are shaping our lives around it, it is of paramount importance to consider those people who struggle in using the new technologies and inventions. In this paper, we are presenting an algorithm and a framework that helps partially sighted people to locate their essential belong- ings. The framework integrates state-of-the-art technologies from computer vision, speech recognition and communication queueing theory to create a framework that can be implemented on low computing power platforms. The framework verbally communicates with the users to identify the object they are aiming to find and then notify them when it is within the range.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-49108-6_34
Uncontrolled keywords: Assistive technologies, Visual impaired, Artificial intelligence, Machine learning, Real-time objects detection, Information freshness
Divisions: Faculties > Sciences > Medway School of Pharmacy
Depositing User: Gurprit Lall
Date Deposited: 14 Jul 2020 16:07 UTC
Last Modified: 15 Jul 2020 11:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82104 (The current URI for this page, for reference purposes)
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