A Framework to Detect Social Distancing Violation and Mask Use in Public Places

Communities are instructed to stop and reduce close contact with nearby residents as the COVID-19 spreads. Social distancing is a useful strategy for halting and limiting the COVID-19 virus's spread. The main cause for worry is that COVID-19 can travel from one person to another through touch or being close to an infected person. This has been a really challenging task given how crowded some public areas are. A social distancing detection tool is devised that can check if people are keeping a safe distance from each other by trying to analyze real-time video streams from the camera using Python, Deep Learning, and Computer Vision. The goal is to create a Python-based system for the instantaneous detection of social distance violations and mask use. To monitor public areas, workplaces, or events, the solution merges security cameras with computer vision techniques and deep learning algorithms. Data collection, preprocessing, mask detection, social distancing detection, integration, testing, and evaluation were among the processes that the project went through. The final solution is a helpful tool for companies and crowded places to ensure that social distance and mask-wearing norms are being followed.