Crowd Counting Model

Crowd counting models use computer vision to automatically estimate the number of people in an image or video, aiming for accurate and efficient crowd density estimation. Recent research focuses on improving accuracy through innovative architectures like those based on CLIP, transformers, and efficient lightweight networks, while also addressing challenges such as limited training data via techniques like unsupervised learning and data augmentation with diffusion models. These advancements are significant for applications ranging from public safety and urban planning to resource management, improving the reliability and scalability of crowd analysis in various contexts.