  Crowd counting is critical for numerous video surveillance scenarios. One of
the main issues in this task is how to handle the dramatic scale variations of
pedestrians caused by the perspective effect. To address this issue, this paper
proposes a novel convolution neural network-based crowd counting method, termed
Perspective-guided Fractional-Dilation Network (PFDNet). By modeling the
continuous scale variations, the proposed PFDNet is able to select the proper
fractional dilation kernels for adapting to different spatial locations. It
significantly improves the flexibility of the state-of-the-arts that only
consider the discrete representative scales. In addition, by avoiding the
multi-scale or multi-column architecture that used in other methods, it is
computationally more efficient. In practice, the proposed PFDNet is constructed
by stacking multiple Perspective-guided Fractional-Dilation Convolutions (PFC)
on a VGG16-BN backbone. By introducing a novel generalized dilation convolution
operation, the PFC can handle fractional dilation ratios in the spatial domain
under the guidance of perspective annotations, achieving continuous scales
modeling of pedestrians. To deal with the problem of unavailable perspective
information in some cases, we further introduce an effective perspective
estimation branch to the proposed PFDNet, which can be trained in either
supervised or weakly-supervised setting once the branch has been pre-trained.
Extensive experiments show that the proposed PFDNet outperforms
state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, WorldExpo'10,
UCF-QNRF, UCF_CC_50 and TRANCOS dataset, achieving MAE 53.8, 6.5, 6.8, 84.3,
205.8, and 3.06 respectively.

Computer Vision and Pattern Recognition