新常态：利用物联网和深度学习及时发现和遏制新冠肺炎的合作范式
New Normal: Cooperative Paradigm for Covid-19 Timely Detection and Containment using Internet of Things and Deep Learning
摘要：
新型冠状病毒（COVID-19）的传播影响全球经济，对政府及卫生当局造成数万亿美元的损失。本研究的目的是引入一种连接的智能模式，不仅可以检测病毒的可能传播，还可以帮助重启企业/经济，恢复社会生活。我们提出了一个基于物联网（IoT）的范例，该范例利用基于卷积神经网络（CNN）、智能可穿戴设备和互联电子健康的目标检测，以避免当前和未来的疫情爆发。首先，连接的监控摄像头将连续的视频流馈送到服务器，在那里我们检测对象间的距离，以识别任何违反社交距离的行为。违规行为会激活对活跃智能手机用户及其当前疾病状态的基于区域的监控。如果出现确诊患者或症状严重者，该系统将跟踪接触和感染者，并采取适当措施。我们使用YOLO（你只看一次）v2和v3评估了所提出的社交距离违规检测方案，并使用Python模拟进行了感染传播跟踪。
英文摘要：
The spread of the novel coronavirus (COVID-19) has caused trillions of dollars in damages to the governments and health authorities by affecting the global economies. The purpose of this study is to introduce a connected smart paradigm that not only detects the possible spread of viruses but also helps to restart businesses/economies, and resume social life. We are proposing a connected Internet of Things ( IoT) based paradigm that makes use of object detection based on convolution neural networks (CNN), smart wearable and connected e-health to avoid current and future outbreaks. First, connected surveillance cameras feed continuous video stream to the server where we detect the inter-object distance to identify any social distancing violations. A violation activates area-based monitoring of active smartphone users and their current state of illness. In case a confirmed patient or a person with high symptoms is present, the system tracks exposed and infected people and appropriate measures are put into actions. We evaluated the proposed scheme for social distancing violation detection using YOLO (you only look once) v2 and v3, and for infection spread tracing using Python simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
