Crowd Analysis

Crowd analysis uses computer vision and machine learning to automatically understand and interpret the behavior of groups of people, primarily aiming to improve crowd management, public safety, and urban planning. Current research focuses on developing accurate and efficient algorithms for crowd counting, density estimation, and movement prediction, often employing deep learning models like convolutional neural networks and transformer-based architectures, along with innovative techniques such as Fourier-guided attention and diffusion models. These advancements are significant because they enable the creation of automated systems for monitoring crowds in real-time, improving safety and efficiency in various settings, from stadiums to hospitals.