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FaceTouch:通过监督的对比学习来检测手对面接触,以帮助追踪传染病

Mohamed R Ibrahim^1,2,3, Terry Lyons^3,4

¹Centre for Data Analysis and Policy, University of Leeds, Leeds, United Kingdom.

|June 13, 2024

概括

一个新的计算机视觉框架FaceTouch自动检测视频中的手对面触摸. 该系统通过分析复杂环境中的身体手势来帮助追踪疾病的传播.

科学领域:

计算机视觉计算机视觉
深度学习 (Deep Learning) 是一种深度学习.
公共卫生公共卫生

背景情况:

呼吸道病毒通过人与人接触传播.
COVID-19凸显了联系人追踪的必要性,以制疾病传播.
缺少手对面接触的自动检测,特别是在现实环境中.

研究的目的:

介绍FaceTouch,一个基于深度学习的计算机视觉框架.
为了使手对面接触在各种环境 (如视频聊天,CCTV) 中的自动检测.
为了解决实时的差距,公共卫生的自动联系监控.

主要方法:

开发了一个计算机视觉框架,利用深度学习子模型进行人类检测和动作分析.
员工监督对比学习用于在定制数据集上进行模型培训.
利用RGB数据和身体手势表现 (例如,手臂运动) 来检测面部触摸,即使面部部分封闭.

主要成果:

在复杂的城市和室内场景中,FaceTouch成功地检测到手对面触摸.
该系统通过分析身体手势,证明了除了简单的手近距离检测之外的实用性.
在未见的数据集上观察到强有力的验证,表明了稳定性和部署潜力.

结论:

FaceTouch提供了一种有效的自动方法来检测手对面接触.
该框架能够处理阻塞和复杂场景,使其适合于现实世界的公共卫生应用.
成功验证为在接触者追踪和疾病预防工作中部署这项技术铺平了道路.

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