  Crowd counting is a challenging task due to the large variations in crowd
distributions. Previous methods tend to tackle the whole image with a single
fixed structure, which is unable to handle diverse complicated scenes with
different crowd densities. Hence, we propose the Adaptive Capacity Multi-scale
convolutional neural networks (ACM-CNN), a novel crowd counting approach which
can assign different capacities to different portions of the input. The
intuition is that the model should focus on important regions of the input
image and optimize its capacity allocation conditioning on the crowd intensive
degree. ACM-CNN consists of three types of modules: a coarse network, a fine
network, and a smooth network. The coarse network is used to explore the areas
that need to be focused via count attention mechanism, and generate a rough
feature map. Then the fine network processes the areas of interest into a fine
feature map. To alleviate the sense of division caused by fusion, the smooth
network is designed to combine two feature maps organically to produce
high-quality density maps. Extensive experiments are conducted on five
mainstream datasets. The results demonstrate the effectiveness of the proposed
model for both density estimation and crowd counting tasks.
