  While the performance of crowd counting via deep learning has been improved
dramatically in the recent years, it remains an ingrained problem due to
cluttered backgrounds and varying scales of people within an image. In this
paper, we propose a Shallow feature based Dense Attention Network (SDANet) for
crowd counting from still images, which diminishes the impact of backgrounds
via involving a shallow feature based attention model, and meanwhile, captures
multi-scale information via densely connecting hierarchical image features.
Specifically, inspired by the observation that backgrounds and human crowds
generally have noticeably different responses in shallow features, we decide to
build our attention model upon shallow-feature maps, which results in accurate
background-pixel detection. Moreover, considering that the most representative
features of people across different scales can appear in different layers of a
feature extraction network, to better keep them all, we propose to densely
connect hierarchical image features of different layers and subsequently encode
them for estimating crowd density. Experimental results on three benchmark
datasets clearly demonstrate the superiority of SDANet when dealing with
different scenarios. Particularly, on the challenging UCF CC 50 dataset, our
method outperforms other existing methods by a large margin, as is evident from
a remarkable 11.9% Mean Absolute Error (MAE) drop of our SDANet.
