  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.
