Real-Time Identification of COVID Norm Violations Based on Machine Learning

Authors

  • Neeraj Sharma Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Vivek Ranjan Assistant Professor, Department of Data Science (CS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Beemkumar N. Professor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
  • Manish Joshi Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

Keywords:

COVID-19, Norm Violations, Faster R-CNN, FaceNet, person localization, face recognition

Abstract

Corona virus disease -2019 (COVID-2019) has influenced many people's habits and encouraged some measure of vigilance in daily life. This paper presents a machine learning technique for detecting and recognizing COVID-19 norm violators, aimed at helping the administration in a populous country like India implement preventive guidelines. Persons are localized using the Faster Region  Convolution Neural Network (R-CNN) model, social distance is measured using a height-width comparison, and a modified Faster R-CNN model is used to identify FaceNet in the proposed framework. Following detection, the program uses a face recognition library based on FaceNet to identify the offenders. Results from evaluating the suggested approach against both comparison datasets and real-world data show that it strikes a better balance between accuracy and complexity than the most recent developments. Because of the complexity of the COVID-19 pandemic, this method provides a simple alternative for monitoring and implementing preventative recommendations.

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Published

04.11.2023

How to Cite

Sharma, N. ., Ranjan, V. ., N., B. ., & Joshi, M. . (2023). Real-Time Identification of COVID Norm Violations Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 406–413. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3721

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

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