We propose an automatic method to identify people who are potentially-infected by droplet-transmitted diseases. This high-risk group of infection was previously identified by conducting large-scale visits/interviews, or manually screening among tons of recorded surveillance videos. Both are time-intensive and most likely to delay the control of communicable diseases like influenza. In this paper, we address this challenge by solving a multi-tasking problem from the captured surveillance videos. This multi-tasking framework aims to model the principle of Close Proximity Interaction and thus infer the infection risk of individuals. The complete workflow includes three essential sub-tasks: (1) person re-identification (REID), to identify the diagnosed patient and infected individuals across different cameras, (2) depth estimation, to provide a spatial knowledge of the captured environment, (3) pose estimation, to evaluate the distance between the diagnosed and potentially-infected subjects. Our method significantly reduces the time and labor costs. We demonstrate the advantages of high accuracy and efficiency of our method. Our method is expected to be effective in accelerating the process of identifying the potentially infected group and ultimately contribute to the well-being of public health.

中文翻译：

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基于近距离交互作用的液滴传播的感染风险排名。

我们提出了一种自动方法来识别可能被飞沫传播疾病感染的人。先前通过进行大规模访问/访谈或手动在成吨的监控录像中进行筛选来识别出这种高风险的感染组。两者都是时间密集的，并且最有可能延迟对流感等传染病的控制。在本文中，我们通过从捕获的监视视频中解决多任务问题来应对这一挑战。这个多任务框架旨在对“近距离互动”原理进行建模，从而推断出个体的感染风险。完整的工作流程包括三个基本子任务：（1）人员重新识别（REID），以通过不同的摄像头识别诊断出的患者和受感染的个人，（2）深度估算，提供所捕获环境的空间知识，（3）姿势估计，以评估已诊断对象和潜在感染对象之间的距离。我们的方法大大减少了时间和人工成本。我们展示了我们方法的高精度和高效率的优势。我们的方法有望在加速识别潜在感染人群的过程中有效，并最终为公众健康做出贡献。