Reducing Viral Transmission through AI-based Crowd Monitoring and Social Distancing Analysis

Abstract

This paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.