Agent-Based Crowd Simulation

Crowd monitoring and simulation combine computer vision and agent-based modeling to track and predict pedestrian dynamics in real time. Data assimilation techniques, particularly particle filters, are used to continuously update agent-based crowd simulations with observational data, bridging the gap between model predictions and real-world measurements. Image processing provides the empirical crowd density and flow information that feeds into these calibration and forecasting pipelines.