CrowdTracing: Overcrowding Clustering and Detection System for Social Distancing

ABSTRACT

Maintaining social distancing in public spaces plays a pivotal role in decreasing COVID-19 contagion and viral spread. COVID-19 has required many countries around the world to close work places, schools and public spaces. This has prompted policy makers, venue managers and local authorities to investigate practical mitigation strategies using technology to exit the lockdown safely and enable the reopening of cities and public spaces. This paper introduces CrowdTracing, a dynamic overcrowding detection system that encourages social-distancing and triggers an alert to venue, city council or facility managers in a dynamic and privacy-preserving manner. CrowdTracing utilises ubiquitous WiFi probing and density-based clustering techniques which can be performed in real-time to identify commonly crowded areas and assist in the estimation of excess gatherings. The proposed system can also be used to enable discovery of where social distancing rules are not being followed, enabling a rapid response, controlling or slowing down the spread of the virus. A classification recall of 0.85 on an experiment with 1000 simulated scenarios were achieved. This indicates the CrowdTracing system proposed was able to identify 85 out 100 scenarios in which social distancing rules were not being followed. © 2021 IEEE.