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SmartEye: An Open Source Framework for Real-Time Video Analytics with Edge-Cloud Collaboration

Authors:

Xuezhi Wang,

Guanyu GaoAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 3767 - 3770

https://doi.org/10.1145/3474085.3478330

Published: 17 October 2021 Publication History

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Abstract

Video analytics with Deep Neural Networks (DNNs) empowers many vision-based applications. However, deploying DNN models for video analytics services must address the challenges of computational capacity, service delay, and cost. Leveraging the edge-cloud collaboration to address these problems has become a growing trend. This paper provides the multimedia research community with an open source framework named SmartEye for real-time video analytics by leveraging the edge-cloud collaboration. The system consists of 1) an edge layer which enables video preprocessing, model selection, on-edge inference, and task offloading; 2) a request forwarding layer which serves as a gateway of the cloud and forwards the offloaded tasks to backend workers; and 3) a backend worker layer that processes the offloaded tasks with specified DNN models. One can easily customize the policies for preprocessing, offloading, model selection, and request forwarding. The framework can facilitate research and development in this field. The project is released as an open source project on GitHub at https://github.com/MSNLAB/SmartEye.

References

[1]

Chien-Chun Hung, Ganesh Ananthanarayanan, Peter Bodik, Leana Golubchik, Minlan Yu, Paramvir Bahl, and Matthai Philipose. 2018. Videoedge: Processing camera streams using hierarchical clusters. In 2018 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 115--131.

Crossref

Google Scholar

[2]

Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, and Ion Stoica. 2018. Chameleon: scalable adaptation of video analytics. In Proceedings of SIGCOMM 2018. 253--266.

Digital Library

Google Scholar

[3]

Yuanqi Li, Arthi Padmanabhan, Pengzhan Zhao, Yufei Wang, Guoqing Harry Xu, and Ravi Netravali. 2020. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics. In Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication. 359--376.

Digital Library

Google Scholar

[4]

Zhujun Xiao, Zhengxu Xia, Haitao Zheng, Ben Y Zhao, and Junchen Jiang. 2021. Towards Performance Clarity of Edge Video Analytics. arXiv preprint arXiv:2105.08694 (2021).

Google Scholar

[5]

Ben Zhang, Xin Jin, Sylvia Ratnasamy, John Wawrzynek, and Edward A Lee. 2018. Awstream: Adaptive wide-area streaming analytics. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication. 236--252.

Digital Library

Google Scholar

[6]

Haoyu Zhang, Ganesh Ananthanarayanan, Peter Bodik, Matthai Philipose, Paramvir Bahl, and Michael J Freedman. 2017. Live video analytics at scale with approximation and delay-tolerance. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). 377--392.

Digital Library

Google Scholar

[7]

Miao Zhang, Fangxin Wang, Yifei Zhu, Jiangchuan Liu, and Zhi Wang. 2021. Towards cloud-edge collaborative online video analytics with fine-grained serverless pipelines. In Proceedings of the 12th ACM Multimedia Systems Conference. 80--93.

Digital Library

Google Scholar

Cited By

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Dong YGao G(2024)EdgeCam: A Distributed Camera Operating System for Inference Scheduling and Continuous Learning2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI61053.2024.00028(225-226)Online publication date: 13-May-2024
https://doi.org/10.1109/IoTDI61053.2024.00028
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3323091
Duan SWang DRen JLyu FZhang YWu HShen X(2023)Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2022.321852725:1(591-624)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/COMST.2022.3218527

Index Terms

SmartEye: An Open Source Framework for Real-Time Video Analytics with Edge-Cloud Collaboration
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Networks
  1. Network architectures
  2. Network services

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Information & Contributors

Information

Published In

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

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Short-paper

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Key research and development program of China
Natural Science Foundation of Jiangsu Province

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Dong YGao G(2024)EdgeCam: A Distributed Camera Operating System for Inference Scheduling and Continuous Learning2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI61053.2024.00028(225-226)Online publication date: 13-May-2024
https://doi.org/10.1109/IoTDI61053.2024.00028
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3323091
Duan SWang DRen JLyu FZhang YWu HShen X(2023)Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2022.321852725:1(591-624)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/COMST.2022.3218527

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