  Crowd counting is an important task that shown great application value in
public safety-related fields, which has attracted increasing attention in
recent years. In the current research, the accuracy of counting numbers and
crowd density estimation are the main concerns. Although the emergence of deep
learning has greatly promoted the development of this field, crowd counting
under cluttered background is still a serious challenge. In order to solve this
problem, we propose a ScaleAware Crowd Counting Network (SACCN) with regional
and semantic attentions. The proposed SACCN distinguishes crowd and background
by applying regional and semantic self-attention mechanisms on the shallow
layers and deep layers, respectively. Moreover, the asymmetric multi-scale
module (AMM) is proposed to deal with the problem of scale diversity, and
regional attention based dense connections and skip connections are designed to
alleviate the variations on crowd scales. Extensive experimental results on
multiple public benchmarks demonstrate that our proposed SACCN achieves
satisfied superior performances and outperform most state-of-the-art methods.
All codes and pretrained models will be released soon.
