earticle

영어

Recently, due to the occurrence of safety accidents in areas with high pedestrian density, local governments have been focusing on introducing systems that can prevent such accidents in advance. Specifically, they have plans to prevent these accidents by predicting pedestrian density in real-time beforehand. There are various methods to measure human density, but using CCTV to measure density has been gaining attention. This is because the infrastructure for CCTV is already well established, eliminating the need to build additional hardware infrastructure. In other words, only software enhancements are needed on the backend. Many algorithms for measuring pedestrian density have been developed, and recently, deep learning-based methods have been particularly prominent. However, most deep learning-based density measurement methods either count the number of pedestrians in the entire input video frame or predict their locations. In practice, though, even if the footage is obtained from the same CCTV camera, it is necessary to measure the density for specific areas within the video separately. This is because the distance from the camera differs for each region within the video, leading to potential discrepancies between the visible density in the video and the actual density. Therefore, this paper proposes a density measurement system that compensates for these variations in distance from the camera across different areas.