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.

近年来，世界各地的可持续智慧城市举措对公民的生活产生了巨大影响，并给社会带来了重大变化。更准确地说，有效管理稀疏资源的数据驱动的智能应用程序正在提供智能、高效和安全的城市运营的未来愿景。然而，持续的COVID-19大流行暴露了现有智慧城市部署的局限性；因此;开发能够提供快速有效机制来限制病毒进一步传播的系统和架构已变得至关重要。能够监测和加强人与人之间的社交距离的主动监测系统可以有效减缓这种致命病毒的传播。在本文中，我们提出了一个基于数据驱动的深度学习的智慧城市可持续发展框架，通过大规模视频监控及时应对 COVID-19 大流行。为了实现社交距离监控，我们使用了三种基于深度学习的实时对象检测模型来检测单目摄像头拍摄的视频中的人物。我们使用真实的视频监控数据集验证了我们系统的性能，以实现有效部署。