Crowd Behavior Analysis and Risk Evaluation for Fire Disaster Prevention Using CCTV Video Data

다국어 초록 (Multilingual Abstract)

This study proposes a fire disaster prevention system that analyzes CCTV video streams to detect multi-crowd movements in real time and evaluate potential risk levels. The system identifies early warning signs by assessing crowd density, movement speed, and directional changes, enabling rapid detection of hazardous situations in densely populated environments. The proposed method extracts motion vectors and behavioral features from sequential video frames to compute a risk index that reflects scene instability. Experimental results using synthetic and real CCTV data demonstrate that the model effectively distinguishes normal, alert, and evacuation states. These findings indicate that the system can provide timely early warnings and support prompt decision-making during fire-related emergencies.