F3S:自由流动发热筛查

Kunal Rao, G. Coviello, Min Feng, Biplob K. Debnath, Wang-Pin Hsiung, M. Sankaradass, Yi Yang, Oliver Po, Utsav Drolia, S. Chakradhar
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引用次数: 5

摘要

识别体温升高的人可以减少或显著减缓COVID-19等传染病的传播。我们提出了一种新的发热筛查系统,F3S,它使用边缘机器学习技术来精确测量自由流动环境下多个个体的核心体温。F3S将视觉摄像头与热像仪数据流实时传感器融合,检测体温升高,它有几个独特的特点:(a)视觉流和热流代表了非常不同的模式,我们通过使用实时分析内容和上下文的新的动态对齐技术,动态地将视觉和热框架中的语义等效区域关联起来,(b)我们通过遮挡跟踪人,在可能的情况下识别眼睛(内眼角)、前额、面部和头部区域,并通过使用优先细化算法提供准确的温度读数。(c)即使在戴口罩、太阳镜或帽子等个人防护设备的情况下,我们也能强烈地检测到体温升高,所有这些设备都可能受到炎热天气的影响,导致温度读数错误。F3S已部署在十多家大型商业机构,为室内和室外环境中的数千名员工和客户提供无接触、自由流动、实时发烧筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
F3S: Free Flow Fever Screening
Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F3S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals in a free-flow setting. F3S performs real-time sensor fusion of visual camera with thermal camera data streams to detect elevated body temperature, and it has several unique features: (a) visual and thermal streams represent very different modalities, and we dynamically associate semantically-equivalent regions across visual and thermal frames by using a new, dynamic alignment technique that analyzes content and context in real-time, (b) we track people through occlusions, identify the eye (inner canthus), forehead, face and head regions where possible, and provide an accurate temperature reading by using a prioritized refinement algorithm, and (c) we robustly detect elevated body temperature even in the presence of personal protective equipment like masks, or sunglasses or hats, all of which can be affected by hot weather and lead to spurious temperature readings. F3S has been deployed at over a dozen large commercial establishments, providing contact-less, free-flow, real-time fever screening for thousands of employees and customers in indoors and outdoor settings.
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