  Crowd counting aims to predict the number of people and generate the density
map in the image. There are many challenges, including varying head scales, the
diversity of crowd distribution across images and cluttered backgrounds. In
this paper, we propose a multi-scale context aggregation network (MSCANet)
based on single-column encoder-decoder architecture for crowd counting, which
consists of an encoder based on a dense context-aware module (DCAM) and a
hierarchical attention-guided decoder. To handle the issue of scale variation,
we construct the DCAM to aggregate multi-scale contextual information by
densely connecting the dilated convolution with varying receptive fields. The
proposed DCAM can capture rich contextual information of crowd areas due to its
long-range receptive fields and dense scale sampling. Moreover, to suppress the
background noise and generate a high-quality density map, we adopt a
hierarchical attention-guided mechanism in the decoder. This helps to integrate
more useful spatial information from shallow feature maps of the encoder by
introducing multiple supervision based on semantic attention module (SAM).
Extensive experiments demonstrate that the proposed approach achieves better
performance than other similar state-of-the-art methods on three challenging
benchmark datasets for crowd counting. The code is available at
https://github.com/KingMV/MSCANet
