Crowd Management

Crowd management research focuses on developing effective strategies and technologies for monitoring and controlling large groups of people, aiming to enhance safety, security, and efficiency in various settings. Current research employs diverse approaches, including AI-powered video analytics, LiDAR-based sensing for privacy-preserving crowd counting, and machine learning models (like convolutional neural networks and XGBoost) for predictive crowd behavior analysis and risk assessment. These advancements have significant implications for improving public safety, optimizing resource allocation in events and public spaces, and mitigating risks associated with misinformation spread and malicious attacks on crowd monitoring systems.