Crowd counting has gained significant popularity due to its practical
applications. However, mainstream counting methods ignore precise individual
localization and suffer from annotation noise because of counting from
estimating density maps. Additionally, they also struggle with high-density
images.To address these issues, we propose an end-to-end model called
Fine-Grained Extraction Network (FGENet). Different from methods estimating
density maps, FGENet directly learns the original coordinate points that
represent the precise localization of individuals.This study designs a fusion
module, named Fine-Grained Feature Pyramid(FGFP), that is used to fuse feature
maps extracted by the backbone of FGENet. The fused features are then passed to
both regression and classification heads, where the former provides predicted
point coordinates for a given image, and the latter determines the confidence
level for each predicted point being an individual. At the end, FGENet
establishes correspondences between prediction points and ground truth points
by employing the Hungarian algorithm. For training FGENet, we design a robust
loss function, named Three-Task Combination (TTC), to mitigate the impact of
annotation noise. Extensive experiments are conducted on four widely used crowd
counting datasets. Experimental results demonstrate the effectiveness of
FGENet. Notably, our method achieves a remarkable improvement of 3.14 points in
Mean Absolute Error (MAE) on the ShanghaiTech Part A dataset, showcasing its
superiority over the existing state-of-the-art methods. Even more impressively,
FGENet surpasses previous benchmarks on the UCF\_CC\_50 dataset with an
astounding enhancement of 30.16 points in MAE.

提出了一种称为 Fine-Grained Extraction Network（FGENet）的端到端模型，直接学习表示个体精确定位的原始坐标，通过引入 Fine-Grained Feature Pyramid（FGFP）来融合特征图并使用多任务组合损失函数（TTC）来减轻标注噪声的影响，实验证明 FGENet 在人群计数中取得了显著的改进。