Abstract
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.
Highlights
The most frequent infectious diseases in humans—and those with the highest potential for rapid pandemic spread—are usually transmitted via droplets during close proximity interactions (Salathé et al, 2010)
We propose a novel framework to automatically evaluate the infection risk based on the principle of Close Proximity Interaction
We describe our method as two main stages: (1) identifying the potentiallyinfected group, (2) modeling close proximity interaction
Summary
The most frequent infectious diseases in humans—and those with the highest potential for rapid pandemic spread—are usually transmitted via droplets during close proximity interactions (Salathé et al, 2010). Such infectious diseases include influenza, common colds, whooping cough, SARSCoV, and many others. It is critical to identify the group of individuals who are in close contact with the diagnosed patient, in order to understand and mitigate the spread of the aforementioned pandemic diseases. Two significant challenges exist for this vision-based method: (1) re-identify the diagnosed patient in non-overlapping monitor cameras and (2) assess the potential risk of infection in the exposed population.
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