tetano
Editor, Senior Moderator
Front Neurorobot. 2020 Jan 21;13:113. doi: 10.3389/fnbot.2019.00113. eCollection 2019. [h=1]Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction.[/h]
Guo S[SUP]1[/SUP], Yu J[SUP]1[/SUP], Shi X[SUP]1[/SUP], Wang H[SUP]1[/SUP], Xie F[SUP]2[/SUP], Gao X[SUP]1[/SUP], Jiang M[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
Copyright ? 2020 Guo, Yu, Shi, Wang, Xie, Gao and Jiang.
[h=4]KEYWORDS:[/h] infection risk ranking; influenza-like infection; multi-person pose estimation; multi-tasking; person re-identification
PMID: 32038220 PMCID: PMC6985151 DOI: 10.3389/fnbot.2019.00113
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Guo S[SUP]1[/SUP], Yu J[SUP]1[/SUP], Shi X[SUP]1[/SUP], Wang H[SUP]1[/SUP], Xie F[SUP]2[/SUP], Gao X[SUP]1[/SUP], Jiang M[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
Copyright ? 2020 Guo, Yu, Shi, Wang, Xie, Gao and Jiang.
[h=4]KEYWORDS:[/h] infection risk ranking; influenza-like infection; multi-person pose estimation; multi-tasking; person re-identification
PMID: 32038220 PMCID: PMC6985151 DOI: 10.3389/fnbot.2019.00113
Free full text