Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment

TL;DR

This paper presents a computer vision-based AI system for monitoring social distancing, capable of large-scale deployment with optional automated camera calibration, real-time processing, and privacy-preserving features.

Contribution

It introduces a flexible social distancing surveillance solution with automated camera calibration and violation assessment metrics for large-scale use.

Findings

Performs satisfactorily across different scenarios

Processes video feeds in real-time

Ensures data privacy by face blurring

Abstract

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.