Abstract

During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site.

中文翻译：

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基于视觉的深度学习异常检测用于疫情防控

摘要

在 COVID-19 大流行期间，建议人们与他人保持社交距离。人们的行为也会被注意到，比如因为生病而躺下，被视为异常情况。本文提出了一种基于深度学习的视觉异常分析系统，用于识别具有各种异常类型的个体。在这项研究中，涉及两种类型的异常检测。首先是监控在开放公共区域坠落时的异常情况。二是测量该地区人们的社交距离，以警告短距离内的个人。通过实施名为 You Only Look Once 的深度模型，可以在广泛的开放空间中准确识别相关异常。实验结果表明，该方法的检测准确率为91%。在社交距离上，实际社交距离是通过计算平面距离来计算的，以确保每个人都能符合规范。将两种功能整合起来，实施环境监测系统，将更容易监测和管理现场与疾病相关的异常情况。