Semi Supervised Crowd Counting

Semi-supervised crowd counting addresses the challenge of accurately estimating crowd density from images with limited labeled data, aiming to reduce the substantial annotation effort required for fully supervised methods. Current research focuses on improving model robustness by incorporating uncertainty calibration techniques and leveraging unlabeled data through self-supervised learning strategies, often employing transformer-based architectures and point-based or density-distribution modeling approaches. These advancements enable more efficient and accurate crowd counting, with implications for applications ranging from surveillance and urban planning to event management and resource allocation.