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

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Abstract

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.

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Acknowledgements

This work is supported by Natural Science Foundation of Shanghai under Grant No. 19ZR1455300, National Natural Science Foundation of China under Grant No. 61806126, and Natural Science Foundation of Hebei Province under Grant No. F2019201451.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Lanjun Liang, Huailin Zhao & Fangbo Zhou

  2. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Qing Zhang & Zhili Song

  3. School of Cyber Security and Computer, Hebei University, Baoding, 071002, China

    Qingxuan Shi

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  1. Lanjun Liang
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  2. Huailin Zhao
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  4. Qing Zhang
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Correspondence to Huailin Zhao.

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Liang, L., Zhao, H., Zhou, F. et al. SC2Net: Scale-aware Crowd Counting Network with Pyramid Dilated Convolution. Appl Intell 53, 5146–5159 (2023). https://doi.org/10.1007/s10489-022-03648-4

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