University of Oulu

W. Zhang et al., "A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices," in IEEE Transactions on Cloud Computing, vol. 9, no. 3, pp. 868-880, 1 July-Sept. 2021, doi: 10.1109/TCC.2019.2894621

A streaming cloud platform for real-time video processing on embedded devices

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Author: Zhang, Weishan1; Sun, Haoyun1; Zhao, Dehai2;
Organizations: 1School of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao, China
2Australian National University, Australia
3College of Computer and Communication Engineering, Beijing University of Science and Technology, Beijing, China
4University of Oulu, Finland
5University of Toronto, Canada
6College of Computer Science and Technology, Fudan University, Shanghai, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2019-12-09


Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and more. However, the limited resources of embedded devices is a challenging issue for effective embedded computing. Most of the existing work on this topic focuses on single device based solutions, without the use of cloud computing mechanisms for parallel processing to boost performance. In this paper, we propose a cloud platform for real-time video processing based on embedded devices. Eight NVIDIA Jetson TX1 and three Jetson TX2 GPUs are used to construct a streaming embedded cloud platform (SECP), on which Apache Storm is deployed as the cloud computing environment for deep learning algorithms (Convolutional Neural Networks — CNNs) to process video streams. Additionally, self-managing services are designed to ensure that this platform can run smoothly and stably, in the form of a metric sensor, a bottleneck detector and a scheduler. This platform is evaluated in terms of processing speed, power consumption, and network throughput by running various deep learning algorithms for object detection. The results show the proposed platform can run deep learning algorithms on embedded devices while meeting the high scalability and fault tolerance required for real-time video processing.

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Series: IEEE transactions on cloud computing
ISSN: 2168-7161
ISSN-E: 2168-7161
ISSN-L: 2168-7161
Volume: 9
Issue: 3
Pages: 868 - 880
DOI: 10.1109/TCC.2019.2894621
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
Funding: This research is supported by the Program on Innovation Method Fund of China (Grant No. 2015010300), the Key Research Program of Shandong Province (No. 2017GGX10140) and also supported by Fundamental Research Funds for the Central Universities.
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