Crowd counting is an important task in computer vision, which has many
applications in video surveillance. Although the regression-based framework has
achieved great improvements for crowd counting, how to improve the
discriminative power of image representation is still an open problem.
Conventional holistic features used in crowd counting often fail to capture
semantic attributes and spatial cues of the image. In this paper, we propose
integrating semantic information into learning locality-aware feature sets for
accurate crowd counting. First, with the help of convolutional neural network
(CNN), the original pixel space is mapped onto a dense attribute feature map,
where each dimension of the pixel-wise feature indicates the probabilistic
strength of a certain semantic class. Then, locality-aware features (LAF) built
on the idea of spatial pyramids on neighboring patches are proposed to explore
more spatial context and local information. Finally, the traditional VLAD
encoding method is extended to a more generalized form in which diverse
coefficient weights are taken into consideration. Experimental results validate
the effectiveness of our presented method.

本文提出了一种在学习局部感知特征集时融入语义信息的方法，使用卷积神经网络将原始像素空间映射为密集的属性特征图，并提出使用邻域补丁上空间金字塔的 LAF 来探索更多的空间上下文和局部信息，通过扩展传统的 VLAD 编码方法，将多样的系数权重考虑进去。实验证明了所提出的方法的有效性。