scholarly journals Social Distancing Detection using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3284-3292
Author(s):  
Ms. K. Kusumalatha
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Precautionary Measure ◽  
Learning Stage ◽  
Social Distancing ◽  
Actual Distance ◽  
Actual Contact ◽  
Infection Spread

The continuous COVID-19 Covid episode has caused a worldwide calamity with its dangerous spreading. due to the shortfall of successful healing specialists and therefore the lack of vaccinations against the infection, populace weakness increments. within the current circumstance, as there aren't any antibodies accessible; hence, social removing is believed to be a sufficient precautionary measure (standard) against the spread of the pandemic infection. the risks of infection spread may be limited by keeping aloof from actual contact among individuals. the rationale for this work is, thusly, to administer a profound learning stage to social distance is additionally executed to create the exactness of the model. Thusly, the popularity calculation utilizes a pre-prepared calculation that's related to an additional prepared the distinguished jumping box centroid's pairwise distances of people are resolved. To appraise social distance infringement between individuals, we utilized an estimation of actual distance to pixel and set a grip. An infringement limit is ready up to assess whether the space esteem breaks the bottom social distance edge. Analyses are done on various video arrangements to check the proficiency of the model. Discoveries show that the created system effectively recognizes folks that walk excessively close and penetrates/abuses social seperation; also, the trade collecting approach upholds the general efficiency of the model. The precision of 91% and 96% achieved by the acknowledgment model without and with move learning, independently. The accompanying precision of the model is 94%

scholarly journals Finding an optimal distance of social distancing for COVID 19

2021 ◽  
Vol 3 (3) ◽  
pp. 206-220
Author(s):  
J Samuel Manoharan
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Disease Transmission ◽  
Research Work ◽  
Computational Method ◽  
Infection Prevention And Control ◽  
Social Distancing ◽  
Control Approach ◽  
True Detection ◽  
The Impact

Social distancing is a non-pharmaceutical infection prevention and control approach that is now being utilized in the COVID-19 scenario to avoid or restrict the transmission of illness in a community. As a consequence, the disease transmission, as well as the morbidity and mortality associated with it are reduced. The deadly coronavirus will circulate if the distance between the two persons in each site is used. However, coronavirus exposure must be avoided at all costs. The distance varies due to different nations' political rules and the conditions of their medical embassy. The WHO established a social distance of 1 to 2 metres as the standard. This research work has developed a computational method for estimating the impact of coronavirus based on various social distancing metrics. Generally, in COVID – 19 situations, social distance ranging from long to extremely long can be a good strategy. The adoption of extremely small social distance is a harmful approach to the pandemic. This calculation can be done by using deep learning based on crowd image identification. The proposed work has been utilized to find the optimal social distancing for COVID – 19 and it is identified as 1.89 meter. The purpose of the proposed experiment is to compare the different types of deep learning based image recognition algorithms in a crowded environment. The performance can be measured with various metrics such as accuracy, precision, recall, and true detection rate.

scholarly journals Managing Social Distancing using Smart Detector

2021 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Saurabh Yadav ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Social Distance ◽  
Learning Models ◽  
Social Distancing ◽  
Detection Model

This paper presents a methodology for social distance detection using deep learning models and algorithms such as YOLO and CNN. Deep learning is one of those technologies which have greatly enhanced the overall experience of the technology that humans use. Deep learning has brought a lot of changes from self-driven cars made by Tesla to the smallest object detection model. Deep learning, artificial intelligence, and machine learning provide a way to be able to put things to use. The purpose of this paper is to be able to implement real-time object detection to detect social distancing.

scholarly journals Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247440
Author(s):  
Adina Rahim ◽  
Ayesha Maqbool ◽  
Tauseef Rana
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Time Of Flight ◽  
Absolute Error ◽  
Face Mask ◽  
Low Light ◽  
Environmental Cleaning ◽  
Social Distancing ◽  
Safety Distance ◽  
Tof Camera

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people’s night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.

scholarly journals Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Catching ◽  
Sara Capponi ◽  
Ming Te Yeh ◽  
Simone Bianco ◽  
Raul Andino
Keyword(s):  
Social Distance ◽  
Large Fraction ◽  
Real Life ◽  
Virus Transmission ◽  
Face Mask ◽  
Social Distancing ◽  
Viral Spread ◽  
The Face ◽  
Asymptomatic Individuals ◽  
High Virus

AbstractCOVID-19’s high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.

Social Distancing Using AI and Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 1816-1822
Author(s):  
Mr. Kiran Mudaraddi
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Open Source ◽  
Suggested Method ◽  
Video Frame ◽  
Social Distancing ◽  
The Social ◽  
The Impact

The paper presents a deep learning-based methodology for detecting social distancing in order to assess the distance between people in order to mitigate the impact of the coronavirus pandemic. The input was a video frame from the camera, and the open-source object detection was pre-trained. The outcome demonstrates that the suggested method is capable of determining the social distancing measures between many participants in a video.

Validating Social Distancing through Deep Learning and VR-Based Digital Twins

2021 ◽  
Author(s):  
Abhishek Mukhopadhyay ◽  
G S Rajshekar Reddy ◽  
Subhankar Ghosh ◽  
Murthy L R D ◽  
Pradipta Biswas
Keyword(s):  
Deep Learning ◽  
Social Distancing ◽  
Digital Twins

scholarly journals Social Distancing and COVID-19: Factors Associated With Compliance With Social Distancing Norms in Spain

2021 ◽  
Vol 12 ◽  
Author(s):  
Estrella Gualda ◽  
Andre Krouwel ◽  
Marisol Palacios-Gálvez ◽  
Elena Morales-Marente ◽  
Iván Rodríguez-Pascual ◽  
...  
Keyword(s):  
Social Distance ◽  
Political Ideology ◽  
Regression Models ◽  
Distance Measures ◽  
Spanish Population ◽  
Personal Hygiene ◽  
Bivariate Analysis ◽  
Conspiracy Theories ◽  
Social Distancing ◽  
Factors Associated

This article describes patterns of compliance with social distancing measures among the Spanish population during the coronavirus disease-2019 (COVID-19) pandemic. It identifies several factors associated with higher or lower compliance with recommended measures of social distancing. This research is part of a 67-country study, titled the International COVID-19 study on Social & Moral Psychology, in which we use a Spanish dataset. Participants were residents in Spain aged 18 or above. The sample comprises 1,090 respondents, weighted to be representative of the Spanish population. Frequencies, correlations, bivariate analysis, and six models based on hierarchical multiple regressions were applied. The main finding is that most Spaniards are compliant with established guidelines of social distance during the pandemic (State of Alarm, before May 2020). Variables associated more with lower levels of compliance with these standards were explored. Six hierarchical multiple regression models found that compliance with social distance measures has a multifactorial explanation (R2 between 20.4 and 49.1%). Sociodemographic factors, personal hygiene patterns, and the interaction between personal hygiene patterns and the support for political measures related to the coronavirus brought significant effects on the regression models. Less compliance was also associated with beliefs in some specific conspiracy theories with regard to COVID-19 or general conspiracy mentality (Conspiracy Mentality Questionnaire, CMQ), consumption patterns of traditional mass media (television, paper newspapers, magazines, and radio) and modern means to get informed (online digital newspapers, blogs, and social networks), political ideology, vote, trust in institutions, and political identification. Among the future lines of action in preventing the possible outbreak of the virus, we suggest measures to reinforce trust in official information, mainly linked to reducing the influence of disinformation and conspiracy theories parallel to the pandemic.

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