International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Survey Paper on Social Spacing using Yolov3 Arman Ali1, Arpit Kashyap2, Er. Anuj Singh3 1UG student of Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of
Engineering and Management Lucknow, Uttar Pradesh, India
2UG student of Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of
Engineering and Management Lucknow, Uttar Pradesh, India
3Associate Professor, Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of
Engineering and Management Lucknow, Uttar Pradesh, India ----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Physical isolation refers to the avoidance of
assessing the compliance levels of the populations to measures such as social distancing to minimize contact transmission in different environment.
close contact with other people as one of the measures to prevent the spread of diseases such as COVID 19. A recent study has shown that using the modern approach of the video analytics based on deep learning algorithms, including YOLOv3, the distances between the individuals can be monitored and controlled appropriately. The following is a list of methods suggested by researchers for enhancing social distancing uses of YOLOv3.
2. LITERATURE SURVEY The use of YOLOv3 in deep learning frameworks has over the past few weeks helped a lot in identifying social distancing monitoring systems. Promising future developments in improving gratitude, attention, and emotion recognition can be achieved by embedding YOLOv3 in object detection to improve surveillance, detection, and enforcement of social distancing measures during the spread of contagious viruses.
1. INTRODUCTION It turns out that the physical isolation of people from each other has become one of the obvious approaches being put into practice to prevent the spread of communicable illnesses such as coronavirus. The importance of innovation to imitate and ensure that the policies on social distancing are followed can be ensured by adopting sophisticated techniques like The YOLOv3 algorithm.
[Error! Reference source not found.] The article under analysis titled “A deep learning-based social distance monitoring framework for COVID-19” is the work of Ahmed et al. (2021) and is published in the Sustainable Cities and Society journal which elaborates a framework concerning the perspective from above and monitoring social distancing in public campus environments. This framework would achieve that through application of deep learning especially in the areas of computer vision and improve the process of monitoring and enforcing social distancing to put down the rate of spread of Covid-19. Thus, the findings of this research may be invaluable for the application of state-ofthe-art approaches, deep learning in this case, for the mitigation of the threats posed by the COVID-19-like phenomena for the public health. Thus, the study, devoted to the analysis of the role of the technologies in monitoring social distance and provision of individuals with recommendations on compliance with preventive measures during infectious disease outbreaks, demonstrates the importance of technological support of population in terms of preventing the spread of infections and protecting people’s health.
Considering the fact YOLOv3 is an effective algorithm for object detection the model has been studied in several researches for applying in social distancing. Scientists have suggested very progressive approaches using YOLOv3 to monitor social distancing. These approaches include identifying people in areas such as business and social setups then estimating their distance toward one another as a method of observing social distancing. Through deep learning frameworks, these systems can easily recognize the human beings using YOLOv3 and track them thereby improving the implementation of the supposed social-distancing measures. More than that, the integration of YOLOv3 with other technologies such as drone, IoT, and AI has also expanded the opportunities of social distancing monitoring systems. They can provide information on violations of social distancing, send notifications, and play an essential role in mitigating the transmission of communicable diseases. Summing up, the proposed social distancing applications based on YOLOv3 present modern way to solve such issues in public health. Such systems incorporating the use of real time surveillance integrating deep learning capability presents a effective means of
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[2] The investigated work from Hou et al. (2020) looks at the possibility of detecting social distancing through deep learning models. To contain the impact of COVID-19, the study presents a methodology for estimating distances using deep learning, whereby distance refers to the extent of separation between
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