Face Mask Detection and Social Distancing Monitoring (original) (raw)
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Face Mask Detection System for COVID_19 Pandemic Precautions using Deep Learning Method
Journal of emerging technologies and innovative research, 2020
The World Health Organization (WHO) has stated that there are two ways in which the spread of COVID 19 virus takes place that are respiratory droplets and physical contact. So, avoiding the spread of this virus need some precautionary steps to be taken that are social distancing and the wearing of masks. Among these two precautions the mask wearing is considered as the important factor for the spread of COVID 19 virus because these droplets can land on any surface. So, to keep track of the people that are wearing mask or not is more important. Here we have presented a mask detection system that is able to detect any type of mask and masks of different shapes from the video streams for following the rules that are applied by the government. Deep learning algorithm is used here and the PyTorch library of python is used for mask detection from the images/video streams. The proposed system is able to detect the mask wearing people and those one who are not wearing the masks.
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
According to data obtained by the World Health Organization, the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. Wearing face masks and following safe social distancing are two of the enhanced safety protocols need to be followed in public places in order to prevent the spread of the virus. To create safe environment that contributes to public safety, we propose an efficient computer vision based approach focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places by implementing the model on raspberry pi4 to monitor activity and detect violations through camera. After detection of breach, the raspberry pi4 sends alert signal to control center at state police headquarters and also give alarm to public. In this proposed system modern deep learning algorithm have been mixed with geometric techniques for building a robust modal which covers three aspects of detection, tracking, and validation. Thus, the proposed system favors the society by saving time and helps in lowering the spread of corona virus. It can be implemented effectively in current situation when lockdown is eased to inspect persons in public gatherings, shopping malls, etc. Automated inspection reduces manpower to inspect the public and also can be used in any place.
Scientific Programming
Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploy...
Covid-19 Face Mask Detection and Social Distancing at Public Places
Zenodo (CERN European Organization for Nuclear Research), 2022
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 affects different peoplein different ways. Most infected people will develop mild to moderate illness and recover without hospitalization. Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. The model uses proposed approach of deep learning, Tensorflow, Keras and OpenCV to detect face masks. This model can be used for safety purposes since it is very resource efficient to deploy. The technique deployed in this model gives an accuracy score of 0.9264.
COVID-19 Face Mask and Social Distancing Detector using Machine Learning
2021
Coronavirus disease that hit us in 2020 is affecting humanity drastically. The only safety measure that we may take against this pandemic is to wear “Face Mask” in public areas and maintain “Social Distancing”. Furthermore, many service providers require customers to use the service only if they wear Mask correctly and maintain social distance, the places include airports, hotels, hospitals, railway stations etc. It's not possible to examine manually at all at ones to look if the rule of wearing Mask and social distance is followed as it consumes high human resources. We proposed COVID-19 Face Mask and Social Distancing Detector System which is a onestage detector, which consists of an artificial neural network to fuse high-level semantic information with multiple feature maps, and a machine learning module to focus on detecting face Mask and social distances simultaneously. In addition, the system will use existing IP cameras and CCTV cameras combined with computer vision to de...
IRJET, 2021
According to data obtained by the World Health Organization (WHO), the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. Wearing face masks and following safe social distancing are two of the enhanced that contributes to public safety, we proposed and efficient computer vision-based approach focused on the real-time automated monitoring of people to detect both safe social violations through cameras. In this proposed system modern deep learning algorithm have been mixed with geometric techniques for building a robust modal which covers three aspects of detection, tracking and validation. Thus, the proposed system favors the society by saving time and helps in lowering the spread of corona virus. It can be implemented effectively in current situation when lockdown is eased to inspect persons in public gatherings, shopping malls, etc. Automated inspection reduces manpower to inspect the public and also can be used in any place.
A Computer Vision System to Help Prevent the Transmission of COVID-19
ArXiv, 2021
The COVID-19 pandemic affects every area of daily life globally. To avoid the spread of coronavirus and retrieve the daily normal worldwide, health organizations advise social distancing, wearing face mask, and avoiding touching face. Based on these recommended protective measures, we developed a deep learning-based computer vision system to help prevent the transmission of COVID19. Specifically, the developed system performs face mask detection, face-hand interaction detection, and measures social distance. For these purposes, we collected and annotated images that represent face mask usage and face-hand interaction in the real world. We presented two different face datasets, namely Unconstrained Face Mask Dataset (UFMD) and Unconstrained Face Hand Dataset (UFHD). We trained the proposed models on our own datasets and evaluated them on both our datasets and already existing datasets in the literature without performing any adaptation on these target datasets. Besides, we proposed a...
Covid 19 Monitoring System Using Social Distancing and Face Mask Detection Using YOLOv5 Algorithm
International journal of innovations in engineering and science, 2022
The World Health Organization (WHO) recommends maintaining social distance and using face masks to prevent the spread of COVID 19 in public places. Government and national health authorities have made physical distances of 2 meters and wearing face masks a mandatory safety measure in crowded or public areas [1]. In this study, a popular CCTV surveillance camera was used to develop a collaborative deep learning network-based neural model for automatically detecting, tracking, and estimating distances between people in a crowd. increase. The proposed model includes social distance monitoring and a YOLOv5based facial recognition framework under difficult conditions such as human disability, restricted visibility, and lighting changes [8]. Determine if the location is at high risk of virus infection and spread. This will help authorities redesign public spaces and take precautions to escalate high-risk areas. I-INTRODUCTION In late December 2019, the first case of coronavirus disease (COVID19) was reported in Wuhan, China. Only a few months later, the 2020 virus was hit by global outbreaks. In May 2020, the World Health Organization (WHO) declared a pandemic. According to WHO estimates released on August 26, 2020, there are 23.8 million people vitiate in 200 countries. The death toll from the virus is, a staggering 815,000. As
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The spread of the Coronavirus has prompted individuals to remain indoors and adhere to COVID-appropriate practices, which include social distance, the use of face masks, hand sanitizers, and other measures to protect themselves against infection. In heavily populated locations with limited resources, it is impossible to physically supervise compliance with these standards. As a result, an automated, lightweight, and powerful video monitoring system is required to make the process more efficient. This paper proposes an extensive and productive solution for performing person detection, social distance detection, face mask detection, and face mask classification using object detection, clustering, and Convolution Neural Networks (CNN) On video datasets, in addition to YOLOv3, density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD), and MobileNetV2 based binary classifier other techniques have also been used to achieve the predicted outcomes. This study also provides parallels for numerous face mask detection and classification models.
IRJET- COVID-19 Face Mask and Social Distancing Detector using Machine Learning
IRJET, 2021
Coronavirus disease that hit us in 2020 is affecting humanity drastically. The only safety measure that we may take against this pandemic is to wear "Face Mask" in public areas and maintain "Social Distancing". Furthermore, many service providers require customers to use the service only if they wear Mask correctly and maintain social distance, the places include airports, hotels, hospitals, railway stations etc. It's not possible to examine manually at all at ones to look if the rule of wearing Mask and social distance is followed as it consumes high human resources. We proposed COVID-19 Face Mask and Social Distancing Detector System which is a onestage detector, which consists of an artificial neural network to fuse high-level semantic information with multiple feature maps, and a machine learning module to focus on detecting face Mask and social distances simultaneously. In addition, the system will use existing IP cameras and CCTV cameras combined with computer vision to detect people without Mask and violence of social distancing. This system provides tools for safety and security without any need for manual surveillance system. The system can be deployed on any infrastructure like Hospitals, Office Premises, Government Offices, Schools and Education Institutes, Construction sites, Airports etc. If deployed correctly, the face mask and social distance detector system we are building could potentially be used to help ensure people safety and the security of others.