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Health Crisis Situation Awareness using Mobile Multiple Modalities

机译:健康危机情况使用移动多种方式的认识

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Responding to health crises requires the deployment of accurate and timely situation awareness. Understanding the location of geographical risk factors could assist in preventing the spread of contagious diseases and the system developed, Covid ID, is an attempt to solve this problem through the crowd sourcing of machine learning sensor-based health related detection reports. Specifically, Covid ID uses mobile-based Computer Vision and Machine Learning with a multi-faceted approach to understanding potential risks related to Mask Detection. Crowd Density Estimation. Social Distancing Analysis, and IR Fever Detection. Both visible-spectrum and LWIR images are used. Real results for all modules are presented along with the developed Android Application and supporting backend.
机译:响应健康危机需要部署准确和及时的情况意识。 了解地理危险因素的位置可以帮助防止传染病的传播和制定的系统,Covid ID,通过基于机器学习传感器的健康相关检测报告的人群采购来解决这个问题。 具体而言,CoVID ID使用基于移动的计算机视觉和机器学习,以多方面的方法来理解与掩模检测相关的潜在风险。 人群密度估计。 社会疏远分析,红外发烧检测。 使用可见光频谱和LWIR图像。 所有模块的实际结果都与开发的Android应用程序一起提供和支持后端。

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