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, (VISION)-I-4 is a smart wearable that helps pBLV in their daily challenges. It enables multiple microservices based on artificial intelligence (AI), 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 (VISION)-I-4 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 adaptation strategies for a seamless video service. We demonstrate the performance of our 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 microservices robustly in time-varying network conditions.

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

Mezzavilla, Marco;
2024-01-01

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, (VISION)-I-4 is a smart wearable that helps pBLV in their daily challenges. It enables multiple microservices based on artificial intelligence (AI), 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 (VISION)-I-4 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 adaptation strategies for a seamless video service. We demonstrate the performance of our 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 microservices robustly in time-varying network conditions.
2024
IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE 2024
5G
testbed
AI
assistive technology
e-health
wearable
edge computing
video streaming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1276377
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