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Reseach Article

Real-Time Image Processing using Edge AI Devices

by Tuan Linh Dang, Gia Tuyen Nguyen, Thang Cao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 43
Year of Publication: 2023
Authors: Tuan Linh Dang, Gia Tuyen Nguyen, Thang Cao
10.5120/ijca2023923236

Tuan Linh Dang, Gia Tuyen Nguyen, Thang Cao . Real-Time Image Processing using Edge AI Devices. International Journal of Computer Applications. 185, 43 ( Nov 2023), 1-7. DOI=10.5120/ijca2023923236

@article{ 10.5120/ijca2023923236,
author = { Tuan Linh Dang, Gia Tuyen Nguyen, Thang Cao },
title = { Real-Time Image Processing using Edge AI Devices },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 43 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number43/32973-2023923236/ },
doi = { 10.5120/ijca2023923236 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:29.747983+05:30
%A Tuan Linh Dang
%A Gia Tuyen Nguyen
%A Thang Cao
%T Real-Time Image Processing using Edge AI Devices
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 43
%P 1-7
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet of Things (IoT) has been increasingly developed worldwide for the last five years. The need for businesses to use Computer Vision and Machine Learning in their IoT devices has grown significantly. These demands can come from using IP cameras, webcams, or anything requiring camera modules. People must transfer data from these devices to different personal computers (PCs) or mobile devices, especially in real-time with low latency, to get the information on time. In this article, we propose an architecture that helps businesses make IoT devices that can stream video and process the data in real-time to satisfy their demands using Edges AI Devices. The architecture is easy to implement and strong to serve. It is a flexible and secure architecture, which has already worked with some accelerators, having high speed and accuracy.

References
  1. J. Fredrik Dahlqvist, Mark Patel, Alexander Rajko, Growing opportunities in the Internet of Things, McKinsey & Company, 2019.
  2. S. Loreto, Salvatore Simon Pietro Romano, Real-Time Communication with WebRTC Peer-to-Peer in the Browser, O’Reilly Media, 2014.
  3. A. Johnston, D. Burnett, WebRTC: APIs and RTCWEB Protocols of the HTML5 Real-Time Web, Digital Codex LLC, 2012.
  4. B. Jansen, T.Goodwin, V. Gupta et al., Performance evaluation of WebRTC-based video conferencing,
  5. J. C Lement, Average global mobile and fixed broadband download & upload speed worldwide 2020, 2020.
  6. B. Garc´ıa, F. Gort´azar, L. L´opez-Fern´andez et al, Analysis of video quality and end-to-end latency in WebRTC, 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, 2016.
  7. A. Ahamed, “iWave Embedding Intelligence” [Online]. Available:https://www.iwavesystems.com/rtsp-mediastreaming- server-on-windows-embedded-compact-7-wec7. [Accessed 12 June 2020].
  8. Coral, Google, [Online]. Available: coral.ai. [Accessed 12 June 2023]
  9. N. Jouppi, C. Young, N. Patil et al, In-Datacenter Performance Analysis of a Tensor Processing Unit, ISCA ’17: The 44th Annual International Symposium on Computer Architecture, Toronto, Canada, 2017.
  10. Introduction to Intel® Deep Learning Deployment Toolkit, [Online]. Available: https://docs.openvinotoolkit.org [Accessed 12 June 2023].
  11. Model Optimizer Developer Guide, [Online]. Available: https://docs.openvinotoolkit.org [Accessed 12 June 2023].
  12. A. Driaba, A. Gordeev, V. Klyachin, Recognition of various objects from a certain categorical set in real time using deep convolutional neural networks, CEUR Workshop Proceedings, vol. 2500, 2019.
  13. C. Jiang, D. Ojika, T. Kurth et al, Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11887 LNCS, pp. 587-600, 2019.
  14. D. Gutierrez, Develop Multiplatform Computer Vision Solutions with Intel Distribution of OpenVINO Toolkit, 2019. [Online]. Available: https://insidebigdata.com/2019/08/26/developmultiplatform- computer-vision-solutions-with-inteldistribution- of-openvino-toolkit/. [Accessed 12 June 2023].
Index Terms

Computer Science
Information Sciences

Keywords

IoT Computer Vision OpenCV Machine Learning AI WebRTC RTSP