监测社交距离的实时监测系统

K. S. Babulal, A. Das, Pushpendra Kumar, D. Rajput, Afroj Alam, Ahmed J. Obaid
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引用次数: 4

摘要

由于冠状病毒可能发生变异,以及与之相关的其他科学因素,专家们认为,COVID-19将在未来几十年与我们同在。因此,必须保持社会距离。在大流行的情况下,本文提出了一种利用深度学习来估计个体之间的距离来检测违反社交距离的机制,以减少COVID-19的影响。本文的重点是利用YOLOv3和faster - rcnn来了解社交距离对COVID-19传播的影响,并提出IFRCNN(改进的更快区域-卷积神经网络)。提出的方法IFRCNN通过行人在街上行走的直播视频进行验证。本文将录制视频的实时更新与违反社交距离记录一起保存在一个地点,即一个地点有多少人保持社交距离。更新将存储在基于云的存储系统中,任何组织或公司都可以在其数字设备中获得该位置的实时更新。
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Real-Time Surveillance System for Detection of Social Distancing
As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region – convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.
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来源期刊
CiteScore
5.20
自引率
0.00%
发文量
18
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