Real-Time Crowd Density Estimation and Stampede Risk Assessment System Using Thermal Camera

Stampede accidents frequently occur in situations where large crowds gather. Although education on how to protect oneself and prevent accidents in dense crowds has been provided, these measures mainly focus on post-accident responses. In contrast, this study proposes a proactive approach to prevent stampede accidents by utilizing thermal cameras to detect the number of people in a space in real-time and calculate the risk of a stampede. The system collects object detection and density estimation results using thermal cameras, considering population density in a specific area, and transmits the estimated results to other devices via serial communication. Thermal imaging technology excels at detecting people with high accuracy even in challenging daytime conditions or low-light environments at night. The data collected from the thermal cameras is continuously updated through machine learning and pattern analysis to assess stampede risk, and the results are provided in real-time via a web interface. This allows safety personnel and managers to effectively monitor high-density areas and take immediate action if necessary. Additionally, the system's web interface provides users with real-time information related to stampede risk. The validity of the proposed system has been demonstrated through field applicability and performance evaluation in real-world environments, contributing to enhanced public safety by preventing stampede accidents in advance.