• Durgesh Aswar
• Abhijeet Adsul
• Rushikesh Gadwe
• Komal Wanzare
• Chandrashekhar Patil

Nowadays, the fear and danger of COVID-19 virus is still great. Manually monitoring social distancing regulations is impractical due to large populations on the move and insufficient task force and resources to manage them. You need a lightweight and robust 24/7 video surveillance system to automate this process. This provides a complete and effective solution for detecting people and social distancing violations. For this purpose, YOLOv3 density-based Application Spatial Clustering was used in the surveillance video datasets. System performance is evaluated in terms of accuracy, F1 score and prediction time, which must be low to be practical.

COVID-19, Social Distancing, YOLO, Real-time, Pandemic, AI/Deep Learning

"Social Distance Violation Alarming System: A Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.1007-1011, May-2023, Available :http://www.jetir.org/papers/JETIRFX06178.pdf

"Social Distance Violation Alarming System: A Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp1007-1011, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06178.pdf