Crowd Localization

Crowd localization aims to accurately pinpoint the locations of individuals within dense crowds, a challenging task due to occlusion and variations in scale and density. Recent research focuses on improving localization accuracy through novel loss functions (e.g., incorporating Mahalanobis distance and optimal transport), enhancing model generalization across diverse crowd scenes (e.g., using dynamic proxy domains), and employing advanced architectures like transformers and anchor pyramid networks for more robust and efficient prediction. These advancements are crucial for applications ranging from security monitoring and crowd analysis to autonomous navigation and human-computer interaction.