The problem of counting crowds in varying density scenes or in different density regions of the same scene, named as pan-density crowd counting, is highly challenging. Previous methods are designed for single density scenes or do not fully utilize pan-density information. We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting. In order to effectively capture pan-density information, PaDNet has a novel module, the Density-Aware Network (DAN), that contains multiple sub-networks pretrained on scenarios with different densities. Further, a module named the Feature Enhancement Layer (FEL) is proposed to aggregate the feature maps learned by DAN. It learns an enhancement rate or a weight for each feature map to boost these feature maps. Further, we propose two refined metrics, Patch MAE (PMAE) and Patch RMSE (PRMSE), for better evaluating the model performance on pan-density scenarios. Extensive experiments on four crowd counting benchmark datasets indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.
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