SFPANet: Separation and fusion pyramid attention network for crowd counting

Crowd counting methods have become increasingly mature. However, the problem of dramatic scale variation still exists. For this reason, we propose an efficient separated and fused pyramid attention network, which can extract multiscale features on channels and space and greatly alleviate the problem of dramatic scale variation. First, in order to extract the rich features on the channel, we design a separated and fused channel attention module, which is composed of two 3x3 convolution layers, a separated attention module, and a SE module. Second, we design a spatial contextual feature fusion module to fully extract multiscale features in spatial dimensions. Finally, we conduct comparison experiments with state-of-the-art methods on several challenging datasets, including the ShanghaiTech, UCF_CC_50, and WorldExpo’10 datasets. The experimental results show our method outperforms most of the state-of-the-art methods. We conduct ablation experiments on the ShanghaiTech Part A and Part B datasets to verify the importance of each submodule.