Citation

Lee, Chia Chie and Lee, Lini and Choo, Kan Yeep (2022) A Deep Learning-Based Social Distancing Surveillance System for The Edge Devices with A FPGA-Based Accelerator. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Abstract

The social distancing policy has been introduced in many countries to stop the spread of COVID-19 disease. This research proposed a deep learning-powered social distancing surveillance system that can monitor the physical distance between individuals in public areas in real-time. The system developed in this research was deployed on the edge device powered by an ARM processor. An FPGA accelerator was added alongside the ARM processor to accelerate the execution of the deep learning inference.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Dec 2022 02:10
Last Modified:	29 Dec 2022 02:10
URII:	http://shdl.mmu.edu.my/id/eprint/11071

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