Inter-Homines: Distance-Based Risk Estimation for Human Safety
This work addresses human safety in crowded areas by providing a tool for monitoring social distancing, but it is incremental as it combines existing computer vision methods with a new parametric risk model.
The authors tackled the problem of estimating contagion risk in monitored areas by developing Inter-Homines, a system that uses RGB cameras to locate people in 3D space, calculate interpersonal distances, and predict risk levels in real-time, validated by epidemiologists for safety monitoring.
In this document, we report our proposal for modeling the risk of possible contagiousity in a given area monitored by RGB cameras where people freely move and interact. Our system, called Inter-Homines, evaluates in real-time the contagion risk in a monitored area by analyzing video streams: it is able to locate people in 3D space, calculate interpersonal distances and predict risk levels by building dynamic maps of the monitored area. Inter-Homines works both indoor and outdoor, in public and private crowded areas. The software is applicable to already installed cameras or low-cost cameras on industrial PCs, equipped with an additional embedded edge-AI system for temporary measurements. From the AI-side, we exploit a robust pipeline for real-time people detection and localization in the ground plane by homographic transformation based on state-of-the-art computer vision algorithms; it is a combination of a people detector and a pose estimator. From the risk modeling side, we propose a parametric model for a spatio-temporal dynamic risk estimation, that, validated by epidemiologists, could be useful for safety monitoring the acceptance of social distancing prevention measures by predicting the risk level of the scene.