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arXiv · 2311.13939

5G Edge Vision: Wearable Assistive Technology for People with Blindness and Low Vision

Tommy Azzino·Marco Mezzavilla·Sundeep Rangan·Yao Wang·John-Ross Rizzo
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Abstract

In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information. From our previous work, VIS4ION is a smart wearable that helps pBLV in their day-to-day challenges. It enables multiple artificial intelligence (AI)-based microservices such as visual scene processing, navigation, and vision-language inference. These microservices require powerful computational resources and, in some cases, stringent inference times, hence the need to offload computation to edge servers. This paper introduces a novel video streaming platform that improves the capabilities of VIS4ION by providing real-time support of the microservices at the network edge. When video is offloaded wirelessly to the edge, the time-varying nature of the wireless network requires the use of adaptation strategies for a seamless video service. We demonstrate the performance of an adaptive real-time video streaming platform through experimentation with an open-source 5G deployment based on open air interface (OAI). The experiments demonstrate the ability to provide the microservices robustly in time-varying network loads.

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BibTeXRIS

Tommy Azzino, Marco Mezzavilla, Sundeep Rangan, Yao Wang, John-Ross Rizzo. 2023-11-23. 5G Edge Vision: Wearable Assistive Technology for People with Blindness and Low Vision. https://arxiv.org/abs/2311.13939

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