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

• Main Authors: Azzino, Tommy, Mezzavilla, Marco, Rangan, Sundeep, Wang, Yao, Rizzo, John-Ross
• Format: Journal Article
• Language: English
• Published: 23.11.2023
• Subjects: Computer Science - Systems and Control
• DOI: 10.48550/arxiv.2311.13939

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.