Monitoring Social Distancing With Real-Time Detection and Tracking

Monitoring Social Distancing With Real-Time Detection and Tracking

Copyright: © 2022 |Pages: 29
DOI: 10.4018/978-1-7998-8793-5.ch005
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Abstract

Social distancing is one of the suggested solutions by the health authorities to reduce the spreading speed of the COVID-19 in public areas. A six-foot physical distancing has been set by the majority of public governors as a mandatory social regulation. However, it is difficult to monitor whether individuals practice the social distancing regulation or not. State-of-the-art technologies, such as computer visions, artificial intelligence, and big data analytics, can help for automated people detection and tracking in the crowd for indoor and outdoor environments using surveillance cameras. In this chapter, several types of popular object detection and tracking schemes in monitoring social distancing are illustrated with implementations of a cutting-edge human detection model by testing its reliability using a sample video. A real-world case study for social control management system is also introduced with its architecture designs and implementations in the context of the COVID-19 pandemic.
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Introduction

The COVID-19 pandemic has completely changed the world with dire consequences to the global healthcare system. It is urgent to control the spread of the disease, and reduce its negative impacts. Developing medications and vaccines is the most effective way to limit the speed of infection, confirmed by epidemiological studies in Chapter 2. Despite the efficiency of pharmaceutical solutions, no appropriate cure or available treatment has been developed until the last quarter of 2020. While medical research scientists keep working on producing effective medications for the new coronavirus, before October, no antiviral drug has been approved for use in the treatment of COVID-19 requiring hospitalization. Veklury is the first anti-COVID treatment approved by the United States Food and Drug Administration (FDA), however, FDA also declares that it does not include the entire population (FDA, 2020). Despite an ongoing research on the vaccine development, no certain vaccine has been reported as of November when Pfizer and BioNTech announced their vaccine against COVID-19 has been achieved a success in the Phase 3 clinical trial (Pfizer, 2020). Given such harsh conditions from March to November, non-pharmaceutical measures, such as stay-at-home orders and social distancing regulations, have been executed as an alternative way to reduce the spread of COVID-19. However, the stay-at-home policy, as concluded in Chapter 4, has been determined as a failure because of an unexpected movement pattern in the United States, thereby, it is more crucial to monitor the social distancing in the public.

Practicing social distancing is one of the most effective precautions to prevent the spread of an infectious disease. It has been referred by the majority of the global communities since the COVID-19 pandemic. The purpose of practicing social distancing is to minimize the proximity of individuals physical contacts in crowded spaces, thus reducing the accumulative infection risk. Initially, the coronavirus is believed to be transmittable through the air only if people sneeze or cough, however, the World Health Organization (WHO) denies this inference in July 2020, and further announces that the new coronavirus can be spread by tiny particles suspended in the air after individuals talk or even breathe in crowded, closed, or poorly ventilated environments (WHO, 2020). Medical research has confirmed that people with mild or even no symptoms can also carry the novel virus, which implies that it is essential for the entire population to practice social distancing (Kim et al., 2020). Suggested by the epidemiologists, individuals must maintain at least 6 feet as a minimum distance between each other during a pandemic (Olsen et al., 2003). As one of the non-pharmacological preventions, practicing social distancing has been proved to be effective and necessary for controlling the transmission of contagious virus, including SARS, H1N1, and COVID-19 (Ferguson et al., 2006; Thu et al., 2020). The objective of practicing social distancing is to reduce transmission rate, thereby flattening the Gaussian curve and spreading cases over a longer time to relieve pressure on the health care capacity (Fong et al., 2020). A sharp spike of cumulative cases will happen without social distancing, and consequently, will lead to a disease control failure with an exponential growth of the death number.

Key Terms in this Chapter

Deep Learning: A broad family of machine learning models based on neural networks. Typical deep learning models are deep neural networks, convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning.

RCNN: A machine learning model for computer vision that can apply selective search to extract features from images.

Computer Vision: An automation technology that makes computers to gain high-level understanding from images and videos throughout acquiring, processing, analyzing, and recognizing digital data by transforming visual images into numerical or symbolic information.

Non-Pharmaceutical Disease Control: A set of actions, apart from medicine and vaccination, that communities can slow down the spread of a disease, a.k.a. non-pharmaceutical interventions (NPIs).

Deepsort: A machine learning model that can detect and track targeted objects in a video.

Social Distancing Monitor: A technology that is designed to warn individuals when they get too close to each other, particularly relying on communications or contacts in short distances.

YOLO: A real-time object detection system that can directly train on complete images to achieve the best detection performance.

Object Detection: A computer vision technique that can recognize objects from image or video.

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