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

TL;DR

Inter-Homines is a real-time system that uses video analysis to assess contagion risk in crowded areas by tracking people and modeling their interactions to inform safety measures.

Contribution

It introduces a novel real-time risk estimation framework combining computer vision and epidemiological modeling for indoor and outdoor environments.

Findings

Effective 3D localization of people in real-time

Dynamic risk maps accurately reflect social distancing compliance

Validated risk model aligns with epidemiological data

Abstract

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.