  Recently, crowd counting is a hot topic in crowd analysis. Many CNN-based
counting algorithms attain good performance. However, these methods only focus
on the local appearance features of crowd scenes but ignore the large-range
pixel-wise contextual and crowd attention information. To remedy the above
problems, in this paper, we introduce the Spatial-/Channel-wise Attention
Models into the traditional Regression CNN to estimate the density map, which
is named as "SCAR". It consists of two modules, namely Spatial-wise Attention
Model (SAM) and Channel-wise Attention Model (CAM). The former can encode the
pixel-wise context of the entire image to more accurately predict density maps
at the pixel level. The latter attempts to extract more discriminative features
among different channels, which aids model to pay attention to the head region,
the core of crowd scenes. Intuitively, CAM alleviates the mistaken estimation
for background regions. Finally, two types of attention information and
traditional CNN's feature maps are integrated by a concatenation operation.
Furthermore, the extensive experiments are conducted on four popular datasets,
Shanghai Tech Part A/B, GCC, and UCF_CC_50 Dataset. The results show that the
proposed method achieves state-of-the-art results.
