Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment
This provides a surveillance tool for public health authorities to enforce social distancing, but it is incremental as it builds on existing detection and tracking methods.
The paper tackles the problem of monitoring social distancing compliance during the COVID-19 pandemic by developing a computer vision system that detects and tracks people to identify distance violations, achieving real-time video processing and addressing privacy concerns with face blurring.
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.