Deep Learning-Based Crowd Safety and Risk Prediction: A Review of Density Estimation, Behavioral Anomaly Detection, and Early Warning Approaches

Ensuring public safety in crowded environments such as railway stations and transportation hubs, religious services, shopping centers and public events is an important topic. Higher density, less-predictable crowd movement, occlusion and changing environmental conditions complicate manual observation and can delay the recognition of dangerous conditions. Deep learning approaches have enabled progress in methods for counting and estimating density in crowds, detecting and tracking human individuals and detecting abnormal behavior but many methods focus on their specific tasks and are not linked with an integrated mechanism for risk anticipation and timely intervention. This article reviews recent work on artificial intelligence-based crowd monitoring with focus on methods for estimating crowd density, lightweight models, detecting behavioral anomaly, predictive crowd analysis and privacy-aware edge-based deployment. The collected studies are reviewed according to major technical categories, and are compared in terms of their goals, deployment issues and reported limitations. The review reveals recurring areas of difficulty concerning extreme crowd density and robustness to environmental changes, generalization across scenes, separate pipelines for different tasks, real-time operation and privacy. With regard to these issues, conceptual Intelli-Watch architecture is sketched to link crowd-state estimation, movement analysis, and risk forecasting with human-oversight and alerting. The architecture is presented as a research direction and has not yet been implemented or validated.