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.
翻译:对许多视频监视情景来说, 人群计数是关键。 此任务中的主要问题之一是如何处理由视觉效应造成的行人大规模变异。 为解决这一问题,本文件提出一种新的神经进化网络人群计数方法, 叫做“ 视觉引导分数差异网络 ” (PFDNet ) 。 通过模拟连续规模变异, 拟议的 PFDNet 能够选择适合不同空间位置的适当的分数膨胀内核圈。 它极大地提高了只考虑离散代表比例表的状态的灵活性。 此外, 通过避免其他方法中使用的多级或多级网络结构, 它在计算上效率更高。 在实践中, 拟议的 PFDNet 是通过在VGG16- Bn骨架上堆叠叠叠成的多层模型构建的。 通过引入新颖的通缩缩放变相操作, PFCFC 能够在某些视野说明指导下处理空间域中的分位变异比比率比率, 实现连续的平流或多级结构结构 QLA,, 在经过培训的行人行人阵列中,, 能够进一步展示我们所了解的变变的内变的内变变的内变。