Data Fusion Pipeline for UAV-Based Real-Time Night Crowd Counting for Public Safety
Kiat Nern Yeo, Yan Ling Lau, Gee Wah Ng · 2024
Performing crowd management in large scale outdoor events at night is a challenging yet essential task for public safety and security purposes. Traditional methods of carrying out crowd counting require the deployment of massive manpower and are unable to provide a reliable count for effective resource and manpower planning. In recent years, deep learning based crowd counting methods trained on static images were introduced. However, there still exist real world challenges of variations in crowd density across the scene, illumination, environmental conditions, and perspective problems which these methods are unable to fully address. This paper attempts to address the problems of varying crowd densities and illumination through a crowd counting pipeline that fuses illumination enhancement processes with crowd density estimation and crowd localisation techniques to achieve improved accuracy for crowd counts from live UAV video feeds. This data fusion pipeline approach has been demonstrated to provide improved count accuracy on both dataset and real-world images compared against standalone state-of-the-art methods.