  Recently the crowd counting has received more and more attention. Especially
the technology of high-density environment has become an important research
content, and the relevant methods for the existence of extremely dense crowd
are not optimal. In this paper, we propose a multi-level attentive
Convolutional Neural Network (MLAttnCNN) for crowd counting. We extract
high-level contextual information with multiple different scales applied in
pooling, and use multi-level attention modules to enrich the characteristics at
different layers to achieve more efficient multi-scale feature fusion, which is
able to be used to generate a more accurate density map with dilated
convolutions and a $1\times 1$ convolution. The extensive experiments on three
available public datasets show that our proposed network achieves
outperformance to the state-of-the-art approaches.
