A Deep learning based approach for Social Distance Monitoring

K. Das, Sagnik Ghosh, Himandri Sekhar Dutta
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Abstract

The COVID-19 pandemic has hit the world at large claiming large number of lives till date leaving us with no solution except maintaining social distancing or washing hands regularly, wearing masks and staying at homes. Social distancing is one of the key aspects to prevent spreading of this virus. It means more of maintaining suitable distance between each other. Artificial intelligence has been used widely for a large number of purposes and as such is one of the key tools used here for implementing this project. The proposed system identifies people who are not suitable distance apart by using object detection and calculating the Euclidian distance between two people. This system would be beneficial to the authorities for alerting people if the situation is serious.
基于深度学习的社交距离监测方法
COVID-19大流行席卷全球,迄今为止夺去了大量生命,除了保持社交距离、经常洗手、戴口罩和呆在家里,我们没有其他解决办法。保持社交距离是防止这种病毒传播的关键方面之一。它更多地意味着彼此之间保持适当的距离。人工智能已被广泛用于许多目的,因此是这里用于实施该项目的关键工具之一。该系统通过物体检测和计算两人之间的欧几里得距离来识别距离不合适的人。这一系统将有利于当局在情况严重时提醒人们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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