Scale Crowd

Scale crowd research focuses on analyzing and modeling large groups of people, aiming to improve understanding of crowd behavior and dynamics for applications like safety management and social media analysis. Current research employs diverse approaches, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and large language models (LLMs), often combined with techniques like Bayesian aggregation and attention mechanisms to address challenges such as bias mitigation, occlusion, and data scarcity. These advancements have significant implications for improving crowd simulation accuracy, enhancing prediction of social media engagement, and developing more effective crowd safety and management strategies.