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

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.

Automated summary

This paper explores the use of multi-person pose estimation techniques to reduce the risk of airborne pathogen transmission. The developed techniques analyze CCTV inputs for crowd analysis and utilize pose feature positions and mask detection to assess interpersonal distance and behavior. By combining multiple models and assessing transmission risk based on scientific literature, a real-time risk density heat-map is displayed. This system has the potential to improve public space management and reduce transmission in future pandemics.