Unconstrained face mask and face-hand interaction datasets: building a computer vision system to help prevent the transmission of COVID-19 (original) (raw)
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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...
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.
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...
Face Mask Detection and Social Distancing Monitoring
International Journal for Research in Applied Science & Engineering Technology, 2021
During pandemic COVID-19, the World Health Organization (WHO) reports suggest that the two main routes of transmission of the COVID-19 virus are respiratory droplets and physical contact. Respiratory droplets are generated when an infected person coughs or sneezes. Any person in close contact (within 1 m) with someone who has respiratory symptoms (coughing, sneezing) is at risk of being exposed to potentially infective respiratory droplets. Droplets may also land on surfaces where the virus could remain viable; thus, the immediate environment of an infected individual can serve as a source of transmission (contact transmission). Wearing a medical mask and social distancing is one of the prevention measures that can limit the spread of certain respiratory viral diseases, including COVID-19. World Health Organization has made wearing masks and also social distancing compulsory to protect against this deadly virus. This paper is about we developed a generic Deep Neural Network-Based model for mask detection, tracking using cameras and also we developed second application for social distancing monitoring application with the help of Python and Computer Vision.
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...
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.
ViDMASK dataset for face mask detection with social distance measurement
Displays, 2022
The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people’s faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK diversifies the subjects in terms of pose, environment, quality of image, and versatile subject characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of most models are less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.
Face Mask and Social Distance Detection
IRJET, 2022
Increasing numbers of cases of COVID-19 provide insight into the pandemic's spread.However, wearing the face mask in order to prevent the transmission of droplets in air and maintaining appropriate physical distance between the people can help to fight this pandemic. This project is helpful in detecting face masks and social distancing on a video feed using object detection and Deep learning. OpenCV, TensorFlow/Keras are the software requirements used to build a Convolutional Neural Network (CNN) model to detect face masks. Face masks will be detected in real-time video and in images. CNN algorithm is used for object detection, image classification and recognition and the specialty of this algorithm is its convolutional ability. YOLO algorithm is popular because of its speed and accuracy of detection of objects and YOLO algorithm abbreviated as "You Only Look Once". This YOLO algorithm detects the people in frame and check the social distancing.
Social Distance Analyzer along with Face Mask Detection using AI and ML
International Journal of Computer Applications
The COVID-19 virus spreads through the midst groups of people who are in close contact for an extended period. The chances of spreading a virus are higher when a person who is infected with the virus sneezes, coughs, or talks near others. It is very important for us to stay a minimum of 6 feet away from other people even if you or they do not have any symptoms. Social distancing is the best technique to be followed to reduce the spread of the virus. People are informed to avoid contact with other people, thereby supervising the spread of the virus. Artificial Intelligence and Deep Learning have shown good outcomes for some daily life problems. Computer vision and deep learning techniques are used to see social distancing between people in public places. It uses the YOLOv3 object recognition paradigm to categorize. The detection algorithm uses a pre-trained algorithm that is associated with an extra trained layer using an overhead human data set. Euclidean distance is used in the detection of bounding box centroid's pairwise distances of people are determined. Accuracy up to 98% is achieved by the detection model. Coronavirus outbreaks can be solved by social distancing as well as putting on a face mask. Wearing a mask as well as the ensuing social distancing would save large numbers of lives. So, Face Mask Detection would be used efficiently for the purpose.
2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 2021
In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, Density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along w...