IJERT-Social Distancing and Face Mask Detection From CCTV Camera (original) (raw)
Related papers
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.
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...
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.
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...
Coronamask: A Face Mask Detector for Real-Time Data
International Journal of Advanced Trends in Computer Science and Engineering, 2020
COVID-19 (2019 novel coronavirus) which started in China had spread all over the world rapidly. It is the worst health crisis the whole world has suffered after World War II. Many precautionary measures have been indicated by the World Health Organisation (WHO) like to maintain social distancing, wear masks, wash hands with soap for 20 seconds and many more. Wearing masks in public places is quite an effective measure to stay protected from this pandemic. There is very few research done for detecting face masks. This paper contributes to the welfare of human beings and proposes CoronaMask, a highly effective face mask detector. The proposed model uses the deep learning convolutional neural network (CNN) algorithm as a base for detecting faces. In this study, the dataset has been created which consists of 1238 images which are divided into two classes as "mask" and "no_mask". This model also takes live streaming videos as input and detects faces which are wearing masks and which are not wearing a mask. The convolutional neural network is trained on the dataset and it gives 95% of accuracy. CoronaMask, a two-phase face mask detector works in identifying masks in images and also in real-time video streams.
Automated Social Distancing and Face Mask Detection System
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
COVID-19 epidemic has fleetly affected our day-today life dismembering the world trade and movements. Wearing a defensive face mask has become a new normal. In the near future, numerous public service providers will ask the guests to wear masks rightly to benefit of their services. Thus, face mask recognition has turn out to be a pivotal task to help global society. This paper presents a simplified approach to achieve this purpose using some introductory Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit-Learn. The projected system detects the face from the image appropriately and then identifies if it has a mask on it or not. As a surveillance task executor, it can also distinguish a face along with a mask in motion. The system attains precision up to95.77 and 94.58 independently on two different datasets. We discover optimized values of parameters using the Convolutional Neural Network model to spot the presence of masks rightly without causing over-fitting.
YOLOv4: A Face Mask Detection System
IRJET, 2022
Corona virus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. The greatest risk of transmission exists. In public locations one of the most efficient methods to be careful is to wear a mask. However, some irresponsible people refuse to wear face mask with so many excuses. Moreover, developing the face mask detector is very crucial in this case. In this work, openCV is utilized to locate people who are wearing masks. Using real-time video processing, we will develop a deep learning model that can be used to evaluate the ratio of people wearing masks to those who aren't in crowded places. We evaluate the video stream using a real-time video camera and issue a notification when the zone contains persons who are not wearing masks. We used YOLOv4 to determine whether the mask is worn correctly on the face. Darknet framework is employ for YOLO training, which defines the network's architecture and aids CPU and GPU processing. We utilized Tkinter from the Python GUI for the user interface.
International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022
COVID-19 pandemic has affected the world gravely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 170 million people causing over 3.6 million deaths [1]. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don't wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.