scholarly journals Social Distancing for Covid-19 Monitoring System

2021 ◽  
pp. 49-61
Author(s):  
Raj Kushwaha ◽  
Kismat Khatri ◽  
Yogesh Mahato
Keyword(s):  
Real Time ◽  
Safe Distance ◽  
Public Places ◽  
Cascade Algorithm ◽  
Facial Mask ◽  
Social Distancing ◽  
Video Footage ◽  
Camera Systems ◽  
Haar Cascade ◽  
Increasing Demand

The battle of corona-virus and mankind is possible to be tackled as long as we maintain the basic norm of social distancing and wearing masks amongst ourselves as it is through our droplets from the respiratory tract that the virus spreads. With the increasing demand for man-force and people requiring to go to their workplaces post lockdown, it is very necessary that we save each other from the virus. In this project, we will go through a detailed explanation of how we can use Python, AI and Deep Learning to monitor social distancing at public places and workplaces are keeping a safe distance from each other by analyzing real-time video streams from the camera and also detect facial mask monitoring using OpenCV and Python. To ensure if people are following social distancing protocols in public places and workplaces, we wanted to develop a tool that can monitor if people are keeping a safe distance from one another, wearing masks or not by processing real-time video footage from the camera. People at workplaces, factories, shops can integrate this tool into their security camera systems and can monitor whether people are keeping a safe distance from each other or not along with that we detect facial mask monitoring using Python with help of haar-cascade algorithm to see whether a person is wearing a mask or not. We are also planning to include thermal screening detection to measure the temperature of the subjects, a dashboard which will display a live report of corona cases around the world. We will also include an alert system that will send a notification to the authorities if the social distancing is not followed or if the temperature exceeds the threshold. The authorities can take suitable measures to isolate the subject and thus prevent the spread of Covid-19.

scholarly journals Automatic Facial Mask Rule Violation Detection using Keras & Tensorflow to Limit COVID-19

2022 ◽  
Vol 10 (12) ◽  
pp. 385-393
Author(s):  
Eayan Francis
Keyword(s):  
Hybrid Technique ◽  
Rule Violation ◽  
Public Places ◽  
Facial Mask ◽  
Social Distancing ◽  
The People ◽  
The World ◽  
Neural Network Classifiers ◽  
High Level ◽  
Cctv Camera

Abstract: COVID-19 is a pandemic disease that spread by itself coming in the contact of people. It was initially started from China and now it has been spread all over the world and many casualties have been occurred. Social distancing commonly known as physical distancing is a non-pharmaceutical approach through which it can be reduced. But social distancing only works when people started wearing mask because it can spread by sneezing even having distance among people. So wearing mask is mandatory to stop spreading this virus at its possible extent. In this paper, it has been intended to identify the people who are wearing mask or not. By the help of CCTV camera it can be recognized at the entrance of various public places such as mall, airport, railway station, mart and many more. If facial mask can be recognized effectively with high level of accuracy then it can become mandatory for people who are violating the rules. The proposed system uses Keras and Tensorflow model for identifying whether people are following the rule or not. Tensorflow is a deep learning methodology through which facial mask can be detected with all kind of situations. Proposed system is able to classify whether a person wear a mask or not, it is also able to identify whether people incorrectly wearing mask i.e. partial wearing. It is mandatory to identify whether people are properly using the mask or not. System identify this kind of situation and classified them accordingly. System uses hybrid technique by combining two algorithms i.e. keras and tensorflow. By combining both the systems it can be identified more precisely to identify the rule violations. Keywords: COVID-19, Facial Mask, Convolutional Neural Network, Classifiers, Machine Learning, Image Processing, Pattern Recognition.

scholarly journals Ensuring social distancing using machine learning

2021 ◽  
Vol 40 ◽  
pp. 03049
Author(s):  
Gaurav Mathurkar ◽  
Chinmay Parkhi ◽  
Manish Utekar ◽  
Pallavi H. Chitte
Keyword(s):  
Global Economy ◽  
Close Contact ◽  
Effective Solution ◽  
Video Streams ◽  
Safe Distance ◽  
Public Places ◽  
Effective Measure ◽  
Social Distancing ◽  
Detection Tool ◽  
Two Phases

COVID-19 (Coronavirus) has affected our daily life and is slackening the global economy. In this fight against the COVID-19, social-distancing is proving to be an effective measure to slow down the transmission of coronavirus. In order to control the spread of transmission of COVID-19, social distancing is the best measure which aims to avoid close contact of people. In order to control the spread of transmission of COVID-19, social distancing is the best measure which aims to avoid close contact of people. With the aim of ensuring social- distancing norms in workplace and public places, we can develop a social-distancing detection tool that monitors if people are staying at a safe distance from each other. This can be done by examining real-time video streams from the cameras. In order to provide an effective solution, we are developing a model which has mainly two phases - firstly object detection phase where people will be detected from the video streams. Secondly showing the statistical analysis in the form of dashboards and sending alerts to concerned authorities to take necessary actions.

scholarly journals Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

2021 ◽  
Vol 11 (2) ◽  
pp. 897-910
Author(s):  
K. Pavani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Cascade Algorithm ◽  
Real Time Traffic ◽  
Accuracy And Precision ◽  
Haar Cascade ◽  
Better Than

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

scholarly journals Face Mask and Social Distancing Detection Using ML Technique

2021 ◽  
Vol 9 (VII) ◽  
pp. 3218-3222
Author(s):  
S. Alshifa
Keyword(s):  
Face Detection ◽  
Social Distance ◽  
Face Mask ◽  
Cascade Algorithm ◽  
Social Distancing ◽  
Main Motive ◽  
The Face ◽  
Haar Cascade

Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.

scholarly journals 598 Patients’ Knowledge and Compliance with Government Guidelines for COVID 19

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
J Eskander ◽  
A Khallaf ◽  
S Zaki ◽  
M Elkawafi ◽  
R Makar
Keyword(s):  
Cognitive Impairment ◽  
Hospital Stay ◽  
Safe Distance ◽  
Essential Information ◽  
Social Distancing ◽  
Viral Spread ◽  
Information Leaflets ◽  
Number Of Patients ◽  
Male Patients ◽  
The Government

Abstract Background Since the outbreak of COVID-19; social distancing and recognized effective precautions were recommended by various governments to fight the viral spread. Our aim was to assess the inpatient knowledge and compliance with the government guidelines during their hospital stay and at their discharge in two different NHS hospitals. Method The study took place in two hospitals: Berrywood hospital, UK and Countess of Chester hospital, UK. We invited inpatients to answer an anonymized questionnaire which was designed to include the contemporary government guidelines. We excluded patients with cognitive impairment and those who were not expected to be discharged within days. Results Out of 209 patients, 50% were male. Patients showed good awareness of the main symptoms of the virus (90%). However, A significant number of patients were not fully aware of the recommended precautions to minimize viral spread (28%) and the method of spread (43%). About 41% did not know the recommended safe distance. Conclusions Despite being aware of the main symptoms of COVID-19, a significant number of patients lack essential information needed to minimize the spread of the virus in the society and hospital. We recommend providing patients with information leaflets and direct advice on admission and discharge.

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