This research investigates discrete crowd behavior through a multi-stage analytical pipeline evolving from linear to circular cyber-physical system. The pipeline comprises four stages: Data Extraction (converting surveillance footage to 3D trajectories using YOLOv5/YOLOv9 + StrongSORT), Simulation (generating synthetic crowd behavior in Unreal Engine with parameters like interest scores and density responses), Visualization (rendering heatmaps and trajectory maps), and Analysis (comparing real and simulated data for validation). Inspired by Professor Ikeda's framework, this was reconceptualized as a circular cyber-physical system where crowd phenomena in physical space are captured as monitoring data, integrated through visualization in cyberspace, analyzed for patterns, and fed back into simulation—creating reciprocal feedback loops between physical observation and digital modeling.