Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

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.

Automated summary

Sustainable smart city initiatives worldwide have impacted citizens' lives and brought changes to society, showcasing the potential of data-driven smart applications. However, the COVID-19 pandemic has exposed the limitations of existing smart city deployments. This paper proposes a data-driven deep learning framework for sustainable smart city development, focusing on timely responses through mass video surveillance to combat the pandemic.