Real-Time Identification of COVID Norm Violations Based on Machine Learning
Keywords:
COVID-19, Norm Violations, Faster R-CNN, FaceNet, person localization, face recognitionAbstract
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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