SC2Net: Scale-aware Crowd Counting Network with Pyramid Dilated Convolution

Accurate crowd counting is still challenging due to the variations of crowd heads. Most of crowd counting methods adopt multi-branch networks to extract multi-scale information. However, these networks are too complex to be optimized. To solve these problems, we propose an efficient scale-aware crowd counting network named SC2Net, which adopts the encoder-decoder framework. The encoder uses the first ten layers of VGG16 to extract the primary feature information. The decoder is mainly consisted of our proposed residual pyramid dilated convolution (ResPyDConv) modules to regress predicted density maps. Specifically, the ResPyDConv module is composed of pyramid dilated convolution (PyDConv). Each PyDConv adopts dilated convolutions with different dilated rates. PyDConv divides feature maps into different groups and extracts multi-scale feature information. Extensive experiments are conducted on ShanghaiTech, UCF_CC_50, UCF_QNRF, and NWPU_Crowd datasets. Qualitative and quantitive results show the superiority of our proposed network to the other state-of-the-art methods.

Automated summary

This paper proposes an efficient scale-aware crowd counting network called SC2Net, which adopts an encoder-decoder framework and residual pyramid dilated convolution modules to extract multi-scale information and regress predicted density maps. Experimental results demonstrate the superiority of our proposed method over other state-of-the-art methods.