Monitoring physical distancing for crowd management: real-time trajectory and group analysis
Caspar A. S. Pouw, Federico Toschi, Frank van Schadewijk, Alessandro, Corbetta

TL;DR
This paper introduces a scalable, privacy-preserving framework for real-time analysis of pedestrian trajectories to monitor physical distancing and group behavior in crowded public spaces, aiding crowd management and safety measures.
Contribution
It proposes a novel, efficient graph-based analysis method for real-time pedestrian tracking data, enabling detailed crowd behavior analysis while respecting privacy.
Findings
Effective detection of distancing violations and group formations.
Comparison of pre-Covid and current crowd behaviors using physics observables.
Framework's applicability to various crowd management scenarios.
Abstract
Physical distancing, as a measure to contain the spreading of Covid-19, is defining a "new normal". Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: "in which conditions non-family members infringed social distancing?", "Are there repeated offenders?", and "How are new crowd management measures performing?". Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we…
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