  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.
