Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infections. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

中文翻译：

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DeepSOCIAL：COVID-19 大流行中的社会距离监测和感染风险评估

保持社交距离是世界卫生组织 (WHO) 推荐的解决方案，以尽量减少 COVID-19 在公共场所的传播。大多数政府和国家卫生当局已将 2 米的物理距离设置为购物中心、学校和其他覆盖区域的强制性安全措施。在这项研究中，我们开发了一个通用的基于深度神经网络的模型，用于使用常见的闭路电视安全摄像头在人群中自动检测、跟踪和估计人与人之间的距离。所提出的模型包括一个基于 YOLOv4 的框架和逆透视图，用于在具有挑战性的条件下进行准确的人员检测和社会距离监控，包括人员遮挡、部分可见性和照明变化。我们还通过对来自移动轨迹的时空数据和违反社交距离的比率进行统计分析来提供在线风险评估方案。我们确定病毒传播和感染可能性最高的高风险区域。这可能有助于当局重新设计公共场所的布局或采取预防措施来缓解高风险区域。在牛津市中心数据集上评估了所提出方法的效率，与三种最先进的方法相比，在准确性和速度方面具有卓越的性能。这可能有助于当局重新设计公共场所的布局或采取预防措施来缓解高风险区域。在牛津市中心数据集上评估了所提出方法的效率，与三种最先进的方法相比，在准确性和速度方面具有卓越的性能。这可能有助于当局重新设计公共场所的布局或采取预防措施来缓解高风险区域。在牛津市中心数据集上评估了所提出方法的效率，与三种最先进的方法相比，在准确性和速度方面具有卓越的性能。