Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud

Osman, Ahmed and Abozariba, Raouf and Asyhari, A. Taufiq and Aneiba, Adel and Hussain, Ambreen and Barua, Bidushi and Azeem, Moazam (2022) Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), December 5th – 7th 2021, Orlando, Florida, USA.

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We present a wireless real-time object detection system utilizing single-board devices, cloud computing platforms and web-streaming. Currently, most inference applications stat- ically perform tasks either on local machines or remote cloud servers. However, devices connected through cellular technolo- gies face volatile network conditions, compromising detection performance. Furthermore, while the limited computing power of single-board computers degrade detection correctness, exces- sive power consumption of machine learning models used for inference reduces operation time. In this paper, we propose a dynamic system that monitors embedded device’s wireless link quality and battery level to decide on detecting objects locally or remotely. The experimental results show that our dynamic offloading approach could reduce devices’ energy usage while achieving high accuracy, real-time object detection.
Index Terms—Machine learning, WebRTC, object detection.

Item Type: Conference or Workshop Item (Paper)
Identification Number:
2 October 2021Accepted
24 January 2022Published Online
Uncontrolled Keywords: Machine learning, WebRTC, object detection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Ahmed Osman
Date Deposited: 06 Dec 2021 10:28
Last Modified: 04 Feb 2022 10:59

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