基于Yolo目标检测算法的社交距离检测系统模型建议

Sudhir Sidhaarthan Balamurugan, Sanjay Santhanam, Anudeep Billa, R. Aggarwal, Nayan Varma Alluri
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引用次数: 1

摘要

保持社会距离是一种控制传染病传播非常有效的方法。社交距离顾名思义就是与他人保持身体上的距离,以减少疾病的传播。这个社交距离检测系统强调使用Open-CV和深度学习技术来监测人们之间的距离。本出版物的重点是通过一种称为目标检测的方法来检测人并计算他们之间的距离。当人与人之间的距离小于标准值时,将显示某些指示和警报。这也反映了违反保持社交距离的次数。
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Model Proposal for a Yolo Objection Detection Algorithm based Social Distancing Detection System
Social Distancing is a procedure that is very effective in controlling the transmission of infectious diseases. Social distancing as the name says is the practice of keeping in distance from others physically, to reduce the spreading of diseases. This Social Distance Detection System brings an emphasis on monitoring the distance between people using technologies namely Open-CV and Deep Learning. This publication focuses on detecting people by a method called object detection and calculating the distance between them. When the distance between people becomes less than the standard value, certain indications and alerts will be displayed. This also indicates the number of Social Distancing violations.
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