부산대학교 도서관

In order to prevent accidents due to overcrowding on mass gatherings, it is crucial to predict and track crowd density in real-time. This paper proposes a system for tracking and monitoring indoor crowd density and their locations based on diverse sensing information collected in indoor environments. The proposed system leverages wireless WiFi signal strength and camera images as input, applying fingerprint location estimation and object detection techniques to predict real-time user locations. The monitored space is divided into predefined-size zones, and the predicted crowd count in each zone is used to calculate the density. A web-based integrated monitoring platform is developed to integrate monitoring of zone-specific density and real-time crowd location estimation. To validate the effectiveness of the proposed system, empirical tests and verifications were conducted in a real environment. The experimental results confirm that the proposed system can accurately predict real-time user locations, promptly detect and monitor overcrowding situations indoors, and notify the relevant users when necessary.