Shorfuzzaman Mohammad, Hossain M Shamim, Alhamid Mohammed F
Department of Computer Science, College of Computers and Information Technology (CCIT), Taif University, Taif, Saudi Arabia.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box: 51178, Riyadh 11543, Saudi Arabia.
Sustain Cities Soc. 2021 Jan;64:102582. doi: 10.1016/j.scs.2020.102582. Epub 2020 Nov 5.
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.
世界各地可持续的智慧城市倡议最近对公民生活产生了重大影响，并给社会带来了巨大变化。更确切地说，能有效管理稀缺资源的数据驱动型智能应用正在为智能、高效且安全的城市运营提供一个未来愿景。然而，当前的新冠疫情揭示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以限制病毒进一步传播的系统和架构变得至关重要。一个能够监测并强制人们保持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新冠疫情。为了实施社交距离监测，我们使用了三种基于深度学习的实时目标检测模型来检测单目摄像头拍摄视频中的人员。我们使用一个真实世界视频监控数据集来验证系统性能，以便有效部署。
