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Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

Authors:

Jovan Stojkovic,

Carlee Joe-Wong,

Maria GorlatovaAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 18, Issue 1

Article No.: 9, Pages 1 - 31

https://doi.org/10.1145/3469033

Published: 05 October 2021 Publication History

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Abstract

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency. CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

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Index Terms

Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Mixed / augmented reality
  2. Distributed computing methodologies
    1. Distributed algorithms

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 18, Issue 1

February 2022

434 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3484935

Editor:
Yunhao Liu
Tsinghua University, China

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Published: 05 October 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 December 2020

Published in TOSN Volume 18, Issue 1

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https://doi.org/10.1109/ACCESS.2024.3375928
Zheng Y(2024)Hybrid YOLOv3 and ReID intelligent identification statistical model for people flow in public placesScientific Reports10.1038/s41598-024-64905-914:1Online publication date: 25-Jun-2024
https://doi.org/10.1038/s41598-024-64905-9
Hao JChen YGan J(2024)Delay-guaranteed Mobile Augmented Reality Task Offloading in Edge-assisted EnvironmentAd Hoc Networks10.1016/j.adhoc.2024.103539161(103539)Online publication date: Aug-2024
https://doi.org/10.1016/j.adhoc.2024.103539
Wu ZGuo KWang LHu MRen S(undefined)A Collaborative Learning-based Urban Low-light Small-target Face Image Enhancement MethodACM Transactions on Sensor Networks10.1145/3616013
https://dl.acm.org/doi/10.1145/3616013

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