  Crowd counting aims to learn the crowd density distributions and estimate the
number of objects (e.g. persons) in images. The perspective effect, which
significantly influences the distribution of data points, plays an important
role in crowd counting. In this paper, we propose a novel perspective-aware
approach called PANet to address the perspective problem. Based on the
observation that the size of the objects varies greatly in one image due to the
perspective effect, we propose the dynamic receptive fields (DRF) framework.
The framework is able to adjust the receptive field by the dilated convolution
parameters according to the input image, which helps the model to extract more
discriminative features for each local region. Different from most previous
works which use Gaussian kernels to generate the density map as the supervised
information, we propose the self-distilling supervision (SDS) training method.
The ground-truth density maps are refined from the first training stage and the
perspective information is distilled to the model in the second stage. The
experimental results on ShanghaiTech Part_A and Part_B, UCF_QNRF, and UCF_CC_50
datasets demonstrate that our proposed PANet outperforms the state-of-the-art
methods by a large margin.
