An Artificial Intelligence Framework for Crowd Surveillance and Risk Mitigation

ABSTRACT Ensuring people's safety in public places is a significant challenge for administrations today. The importance of automated crowd‐monitoring systems has recently expanded beyond their role in addressing security concerns in densely populated areas. These systems have become increasingly vital for safeguarding human lives by helping to mitigate the spread of lethal infectious viruses, such as H3N2, SARS‐CoV‐2, Influenza, and COVID‐19. Artificial intelligence (AI) has added a new dimension to this effort by addressing novel and real‐world human safety challenges through automated crowd‐monitoring frameworks. The proposed AI framework for crowd surveillance (AIFCS) employs a deep C2DN network to count people and issue warning signals for images exceeding a specified crowd threshold. Four datasets, including three publicly available ones (Mall, Beijing‐BRT, and SmartCity) and one self‐constructed dataset (Indiana), were used to evaluate the alarm‐based congestion monitoring efficiency. The people‐counting results for highly crowded frame detection accuracy on the Mall, Beijing‐BRT, SmartCity, and Indiana datasets were 98.21%, 86.23%, 75.0%, and 87.01%, respectively. The proposed AIFCS framework ensures real‐time predictions across diverse sequences to prevent overcrowding in public places.