Crowd Counting

Crowd counting, the automated estimation of the number of individuals in images or videos, aims to improve accuracy and robustness across diverse conditions. Current research emphasizes developing models resilient to adverse weather, variations in camera viewpoints and resolutions (including gigapixel images), and noisy or sparse annotations, often employing convolutional neural networks, transformers, and generative adversarial networks. These advancements have significant implications for applications such as urban planning, crowd management, and public safety, particularly in scenarios with limited resources or challenging visual conditions.