  Social distancing has been suggested as one of the most effective measures to
break the chain of viral transmission in the current COVID-19 pandemic. We
herein describe a computer vision-based AI-assisted solution to aid compliance
with social distancing norms. The solution consists of modules to detect and
track people and to identify distance violations. It provides the flexibility
to choose between a tool-based mode or an automated mode of camera calibration,
making the latter suitable for large-scale deployments. In this paper, we
discuss different metrics to assess the risk associated with social distancing
violations and how we can differentiate between transient or persistent
violations. Our proposed solution performs satisfactorily under different test
scenarios, processes video feed at real-time speed as well as addresses data
privacy regulations by blurring faces of detected people, making it ideal for
deployments.
