Crowd counting is a key aspect of crowd analysis and has been typically accomplished by estimating a crowd-density map and summing over the density values. However, this approach suffers from background noise accumulation and loss of density due to the use of broad Gaussian kernels to create the ground truth density maps. This issue can be overcome by narrowing the Gaussian kernel. However, existing approaches perform poorly when trained with such ground truth density maps. To overcome this limitation, we propose using conditional diffusion models to predict density maps, as diffusion models are known to model complex distributions well and show high fidelity to training data during crowd-density map generation. Furthermore, as the intermediate time steps of the diffusion process are noisy, we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning. In addition, owing to the stochastic nature of the diffusion model, we introduce producing multiple density maps to improve the counting performance contrary to the existing crowd counting pipelines. Further, we also differ from the density summation and introduce contour detection followed by summation as the counting operation, which is more immune to background noise. We conduct extensive experiments on public datasets to validate the effectiveness of our method. Specifically, our novel crowd-counting pipeline improves the error of crowd-counting by up to $6\%$ on JHU-CROWD++ and up to $7\%$ on UCF-QNRF.

翻译：人群计数是人群分析的关键环节，通常通过估计人群密度图并对密度值求和来实现。然而，该方法因使用宽高斯核生成真实密度图而导致背景噪声累积和密度损失。缩小高斯核可克服此问题，但现有方法在使用此类真实密度图训练时表现不佳。为突破这一局限，我们提出利用条件扩散模型预测密度图——扩散模型因能良好建模复杂分布且在生成人群密度图时对训练数据保持高保真度而著称。此外，由于扩散过程的中间时间步存在噪声，我们在训练阶段引入回归分支直接估计人群数量以增强特征学习。同时，基于扩散模型的随机特性，我们创新性地生成多张密度图以提升计数性能，这与现有计数流程存在本质差异。我们还摒弃传统密度求和法，提出先进行轮廓检测再求和作为计数操作，该方法对背景噪声更具鲁棒性。我们在公开数据集上开展大量实验验证方法有效性。具体而言，本文提出的新型人群计数流程在JHU-CROWD++上使计数误差降低最高6%，在UCF-QNRF上降低最高7%。