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In this document, we report our proposal for modeling the risk of possiblecontagiousity in a given area monitored by RGB cameras where people freely moveand interact. Our system, called Inter-Homines, evaluates in real-time thecontagion risk in a monitored area by analyzing video streams: it is able tolocate people in 3D space, calculate interpersonal distances and predict risklevels by building dynamic maps of the monitored area. Inter-Homines works bothindoor and outdoor, in public and private crowded areas. The software isapplicable 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 detectionand localization in the ground plane by homographic transformation based onstate-of-the-art computer vision algorithms; it is a combination of a peopledetector and a pose estimator. From the risk modeling side, we propose aparametric model for a spatio-temporal dynamic risk estimation, that, validatedby epidemiologists, could be useful for safety monitoring the acceptance ofsocial distancing prevention measures by predicting the risk level of thescene.

Inter-Homines: Distance-Based Risk Estimation for Human Safety