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      Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

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          Highlights

          • Monitoring social distancing in sustainable smart cities using deep learning.

          • Use of real-time object detection models such as YOLO, SSD, and Faster R-CNN.

          • Capture videos from a monocular camera in mass video surveillance.

          • Perspective transformation of real-time video to transform into bird’s eye view.

          • Experimental results with video surveillance data to show framework efficacy.

          Abstract

          Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society. More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations. However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment; hence; the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount. An active surveillance system capable of monitoring and enforcing social distancing between people can effectively slow the spread of this deadly virus. In this paper, we propose a data-driven deep learning-based framework for the sustainable development of a smart city, offering a timely response to combat the COVID-19 pandemic through mass video surveillance. To implementing social distancing monitoring, we used three deep learning-based real-time object detection models for the detection of people in videos captured with a monocular camera. We validated the performance of our system using a real-world video surveillance dataset for effective deployment.

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          Most cited references20

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          The Pascal Visual Object Classes (VOC) Challenge

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            MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

            We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
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              Strategies for mitigating an influenza pandemic

              Pandemic flu: talking tactics Numerical models of the epidemiology of a potential flu pandemic show there is no single magic bullet which can control the outbreak, but that a combination of approaches could reduce transmission and save many lives. Border restrictions are unlikely to have much effect and travel restrictions within one country would make very little difference to the spread of a pandemic within that country. The models predict that a pandemic in the United Kingdom would peak within two to three months of the first case, and be over within 4 months. It also shows that vaccines need to be available within two months of the start of a pandemic to have a big effect in reducing infection rates. That means that vaccines would need to be stockpiled in advance to be effective. Supplementary information The online version of this article (doi:10.1038/nature04795) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Sustain Cities Soc
                Sustain Cities Soc
                Sustainable Cities and Society
                Elsevier Ltd.
                2210-6707
                2210-6715
                5 November 2020
                5 November 2020
                : 102582
                Affiliations
                [a ]Department of Computer Science, College of Computers and Information Technology (CCIT), Taif University, Taif, Saudi Arabia
                [b ]Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box: 51178, Riyadh 11543, Saudi Arabia
                Author notes
                [* ]Corresponding author at: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box: 51178, Riyadh 11543, Saudi Arabia
                Article
                S2210-6707(20)30800-3 102582
                10.1016/j.scs.2020.102582
                7644199
                33178557
                44ef7959-ec0c-4148-991f-68dbd0df44d6
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 10 August 2020
                : 13 October 2020
                : 27 October 2020
                Categories
                Article

                sustainable cities,covid-19 pandemic,video surveillance,social distancing,deep learning,object detection

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