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Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment;hence;the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount An active surveillance system capable of monitoring and enforcing social distancing between people can effectively slow the spread of this deadly virus In this paper, we propose a data-driven deep learning-based framework for the sustainable development of a smart city, offering a timely response to combat the COVID-19 pandemic through mass video surveillance To implementing social distancing monitoring, we used three deep learning-based real-time object detection models for the detection of people in videos captured with a monocular camera We validated the performance of our system using a real-world video surveillance dataset for effective deployment © 2020 Elsevier Ltd
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Sustainable_Cities_and_Society
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Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic
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