Crowd Counting Method

Crowd counting methods use computer vision to automatically estimate the number of people in an image or video, aiming for accurate and robust performance across diverse scenarios. Recent research focuses on improving accuracy in challenging conditions (e.g., adverse weather, high density), leveraging techniques like contrastive learning, auxiliary point guidance for improved point-based methods, and semi-supervised learning to reduce annotation burden. These advancements, often employing convolutional neural networks (CNNs) and increasingly transformers, have significant implications for applications such as urban planning, security, and event management.