Social Distancing Monitoring Research Articles

Smart resilience through IoT‐enabled natural disaster management: A COVID‐19 response in São Paulo state

AbstractNatural disaster management approach establishes stages of prevention, preparation, response, and recovery. With the Internet of Things (IoT), Bigdata, Business Intelligence, and other Information Communication Technologies, data can be gathered to support decisions in stages of the response to natural disaster events. In biological natural disasters, the ICTs can also support efforts to promote social distancing, public health, and economic monitoring to face the threads. São Paulo state used IoT in scenarios to face COVID‐19, such as monitoring vehicular interurban mobility, social distancing, and economic activity. Frameworks, strategies, data views, and use cases are presented to support the decision‐making process to face this biological natural disaster. The data‐driven approach supports several purposes, including the communication of social distancing indices, economic recovery, the progression of contagion, and deaths. It also played a pivotal role in fostering transparency initiatives for society and supporting the crisis committee by facilitating situational analyses, and this approach became standard practice for pandemic response. Studies and innovative visualisation perspectives have produced positive outcomes, guiding the decision‐making process through data analysis. Noteworthy use cases were interurban traffic fence monitoring; mapping of virus spreading; tracking the economic impact concerning recovery plans; and, evaluating the effectiveness of public policies.

Design of Smart IoT Health and Social Distancing Monitoring System

Since the outbreak of the COVID-19 pandemic, many companies have started to work from home. As the pandemic recovers, companies slowly start to adapt to the situation by making the workers work from the office. This is an effort to reduce the risks of spreading diseases in the workplace during the endemic of COVID-19. An effective solution is urgently needed to reduce and control the transmission rate of COVID-19. This has motivated us to design an Internet of Things (IoT) Health and Social Distancing Monitoring System (IHDS). This system aims to support that initiative and introduce a system that can control the risks of infection among workers due to a worrying spike in the number of cases in the workplace. The proposed system monitors the health condition of the users and controls social distancing at the workplace by using IoT technology and machine learning. Extensive experiments were conducted to assess the performance of the proposed system. Four critical health metrics were closely monitored: body temperature, pulse rate, blood oxygen saturation, and cough detection, achieving impressive accuracy rates of 99.91%, 94.32%, 99%, and 80.5%, respectively. The proposed system initiates the assignment of red boxes to couples who are separated by less than 1 meter, while it designates green boxes for couples who maintain more than a 1-meter distance from each other.

An end-to-end smart IoT-Driven navigation for social distance monitoring framework for Covid-19 using deep convolutional neural networks in Deep Learning

The extraordinary worldwide spread of the COVID-19 coronavirus pandemic has considerably boosted the development of new Internet-of-things (IoT)-based strategies to stop, stop, monitor, or foresee virus propagation among people. These technologies are being used more often for a variety of practical purposes that include enhanced safety, discipline, and control. The novel coronavirus creates a global pandemic and affects a human, besides a huge death rate. As there is no proper medication, at least the spread should be stopped or it should be minimized. One way is to avoid physical contact. People should maintain a minimum distance to avoid direct contact. This paper presents detecting the social distance between pedestrians in public places. The idea is to take live videos from the camera we set up in a public place and create bounding boxes for each person in the video. If two or more people are close to each other violating social distance, then we put a red box around them otherwise we put a green box. The proposed framework's experimental results exhibit good results in monitoring the distance among humans in public areas compared to the existing methods.

Digital Twins in Healthcare: an architectural proposal and its application in a social distancing case study.

The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.

Real-time social distance monitoring and face mask detection based Social-Scaled-YOLOv4, DeepSORT and DSFD&MobileNetv2 for COVID-19

Real-time social distance monitoring and face mask detection based Social-Scaled-YOLOv4, DeepSORT and DSFD&MobileNetv2 for COVID-19

Synthesis of deep learning technique for social distance monitoring in pandemic areas

Synthesis of deep learning technique for social distance monitoring in pandemic areas

Edge-Oriented Social Distance Monitoring System Based on MTCNN

Social distance monitoring (SDM) systems are vital in fighting the spread of the coronavirus (COVID-19). Existing SDM systems employ bounding box-method, which imposes inaccurate distance estimation due to the high variance in its output coordinates. To solve this problem, an SDM system based on multitask cascaded convolutional neural networks (MTCNN) is proposed. Instead of using bounding box coordinates, face detection and facial landmarks localization of MTCNN is used to provide fixed coordinates and increase the distance estimation accuracy of SDM. However, while the accuracy issue is solved by using MTCNN, the SDM system suffer from large computational requirements due to the cascaded networks added on top of the distance estimation process. To deal with this challenge, a constrained optimization technique is employed to each stage of MTCNN with the goal of reducing its hardware requirements while keeping the same reliability as the original implementation. Experimental results show that the SDM system based on the optimized MTCNN achieves higher accuracy performance with reduced computational requirements as compared to conventional SDM systems. This allows the proposed SDM system using optimized MTCNN to be deployed efficiently on edge devices.

Design and Development of IOT Social Distancing and Monitoring Robot for Queue

COVID-19 is the fifth pandemic to be documented since the 1918 flu pandemic. The virus spreads swiftly and evolves over time among humans because it is highly contagious. Since the virus is known to spread airborne, few of the most efficient ways to protect ourselves are by following social distance among individuals and to wear a face mask or shield at all times. These 2 conditions are not followed by some individuals even if the rules are set to be strict and it is impossible for the government to watch over everyone at all times. Companies and other sectors has to be allowed to operated even during the crisis due to economic factors. This is a risky situation for both the companies and the people. As a solution for this situation, a prototype is created to assist with social distance monitoring in public areas to reduce the risk of disease spread. The robot operates by using an Arduino MEGA, IR Sensors and Ultrasonic sensors. The robot comes with a robust and compact design to work in most places with the ability to follow a pre-determined path which gives it a tremendous market potential in all sorts of sectors that involves high number of employees or people.

A Social Distance Monitoring Method Based on Improved YOLOv4 for Surveillance Videos

Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos.

A Conceptual Real-Time Deep Learning Approach for Object Detection, Tracking and Monitoring Social Distance using Yolov5

Objectives: To develop a computer vision-based model that can detect, track and recognize individuals for the purpose of measuring social distance in road traffic videos using surveillance cameras. Methods: The real-time traffic surveillance webcam dataset was applied to validate the model, with better performance metrics outperformed to those of comparable cuttingedge models such as RetinaNet, Faster R-CNN and SSD. Our proposed methodology utilized object detection methods to recognize individuals followed by multiple objects tracking approach to track identified individuals using detected bounding boxes. Our research shows that the conventional method is successful in detecting persons who violate social distances. Findings: Our finding shows that our proposed object detection model successfully recognizes human and those who violating the social distancing measurements. For the purpose of detecting social distance, develop a highly accurate detection technique. Our YOLOv5 with multiple objects tracking algorithm delivered great outcomes with appropriate Precision of 93%, Recall of 94%, F1-Score of 96% and mAP of 95% measures given by object categorization and localization to measure social distancing in real-time traffic surveillance videos. The YOLOv5 model’s results are then compared to many other prominent state-of-the-art models. Novelty: The YOLOv5 and MOTSORT is appropriate for finding whether people are maintaining social distancing or not, intended to identify, monitor and track those who are not following or violating social distances with overall accuracy and efficiency. Keywords: Object Detection; Deep Learning; Social distance monitoring; multiple objects tracking; YOLOv5

SELF CIRCUITRY PERCEPTION FOR PHYSICAL ENVIRONS

The internet of things in the 22nd generation can fight against COVID -19 . COVID -19 is a global pandemic which has brought general changes to human life. COVID -19virus or viral disease can be reduced with the help of social distancing and health monitoring. social distancing can reduce the risk of COVID -19.to maintain physical distance between people, we have made a project which will help people to maintain a minimum distance between them using different Iot sensors and Arduino microcontroller boards. Then people health condition monitoring SPO2 sensor used oxygen level .The infrared temperature sensor appears to be rather straightforward point ,press the button and read the temperature in case of abnormal status GPS Tracking systems enables a base station to keep track of the person current status and navigation system helps the driver to reach the destination and monitoring physical distancing will be part of our life till COVID -19 is under control and this project will help to maintain a secure distance from others.

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Social distance monitoring of site workers for COVID-19 using context-guided data augmentation, deep learning, and homography transformation

Because of the COVID-19 pandemic, many industries have developed efforts to minimize COVID-19’s spread. For example, the construction industry in Melbourne practices social distancing and downsizes the number of workers on the job site. The surveillance system integrated with deep learning models has been extensively utilized to enhance construction safety. However, such 2D-based approaches suffer from occlusions, and the workers may not be accurately detected under this circumstance. To this end, this paper proposes a novel context-guided data augmentation method to enhance deep learning models’ performance under occlusions. The context-guided method can automatically augment images by adding occlusions to the objects. Using this way, deep learning models can learn the object’s features in various occlusion scenarios. Later, this method is validated by a real-time social distancing violation detection system. Specifically, this system utilizes a modified YOLOv4 model to detect workers by bounding boxes. Then, the DeepSORT algorithm is used to track the worker trajectories. Finally, homography transformation is used to calculate the distance between workers in each frame. The system has revealed robust results using the data augmentation method, and promising results indicate that the system can well support worker health during COVID-19.

Vehicular networks for combating a worldwide pandemic: Preventing the spread of COVID-19.

As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people’s social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a better response to a pandemic outbreak. Therefore, researchers recently started developing ideas to stop or at least reduce the spread of the pandemic. While there have been some prior works on wireless networks for combating a pandemic scenario, vehicular networks and their potential bottlenecks have not yet been fully examined. Furthermore, the vehicular scenarios can be identified as the locations, where the social distancing is mostly violated. With this motivation, this article provides an extensive discussion on vehicular networking for combating a pandemic. We provide the major applications of vehicular networking for combating COVID-19 in public transportation, in-vehicle diagnosis, border patrol and social distance monitoring. Next, we identify the unique characteristics of the collected data in terms of privacy, flexibility and coverage, then highlight corresponding future directions in privacy preservation, resource allocation, data caching and data routing. We believe that this work paves the way for the development of new products and algorithms that can facilitate the social life and help controlling the spread of the pandemic.

Face mask detection and social distance monitoring system for COVID-19 pandemic.

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF

Data Safety Prediction Using Bird’s Eye View and Social Distancing Monitoring for Penang Roads

In terms of fatalities, Malaysia ranks third among ASEAN countries. Every year, there is an increase in accidents and fatalities. The state of the road is one factor contributing to near misses. A near miss is an almost-caused accident, an unplanned situation that could result in injury or accidents. The Majlis Bandar Pulau Pinang (MBPP) has installed 1841 closed-circuit television (CCTV) cameras around Penang to monitor traffic and track near miss incidents. When installing CCTVs, the utilisation of video allows resources to be used and optimised in situations when maintaining video memories is difficult and costly. Highways, industrial regions, and city roads are the most typical places where accidents occur. Accidents occurred at 200 per year on average in Penang from 2015 to 2017. Near misses are what create accidents. One of the essential factors in vehicle detection is the “near miss.” In this study, You Only Look Once version 3 (YOLOv3) and Faster Region-based Convolutional Neural Network (Faster RCNN) are used to solve transportation issues. In vehicle detection, a faster RCNN was used. Bird’s Eye View and Social Distancing Monitoring are used to detect the only vehicle in image processing and observe how near misses occur. This experiment tests different video quality and lengths to compare test time and error detection percentage. In conclusion, YOLOv3 outperforms Faster RCNN. In high-resolution videos, Faster RCNN outperforms YOLOv3, while in low-resolution videos, YOLOv3 outperforms Faster RCNN.

Improving the Effectiveness of Anti-COVID Measures in Buildings: Learning from Users’ Perception

The COVID-19 spread abruptly changed the fruition of indoor environments, where necessary adaptive measures have since been implemented. Buildings open to the public were suddenly equipped with physical devices aiming to encourage users’ appropriate behaviors, such as hand sanitizing, social distancing, and temperature monitoring. Through a twofold architectural-psychological perspective, the paper presents a research aiming to understand how users perceive these devices in the Italian context and to identify the design features that could improve their effectiveness in enhancing individuals’ awareness. With an interdisciplinary approach, four methods were adopted: observational field surveys, background and normative framework analysis, survey research through an online questionnaire, and case studies survey research. The results confirm the overall effectiveness of the implemented anti-COVID strategies, their suitability in encouraging individuals’ appropriate behaviors, and the importance of regulating the users’ flow indoors. The research allowed defining the devices (hand-sanitizing devices and temperature-measurement instruments) and wayfinding systems more suited to be included in the prevention strategy and identified their more appropriate design features in relation to the users’ feedback. Operational suggestions are presented as well. The adopted experimental approach can be useful in supporting decision making in managing of the built environment in both the current and future contexts.

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey.

Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors’ best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.

Artificial Intelligence Based Social Distance Monitoring in Public Areas

COVID-19 is an infectious disease caused by a newly discovered coronavirus called SARS-CoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence-based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning.