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Skip-connection convolutional neural network for still image crowd counting

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Abstract

In recent years, crowd counting in still images has attracted many research interests due to its applications in public safety. However, it remains a challenging task for reasons of perspective and scale variations. In this paper, we propose an effective Skip-connection Convolutional Neural Network (SCNN) for crowd counting to overcome the issue of scale variations. The proposed SCNN architecture consists of several multi-scale units to extract multi-scale features. Each multi-scale unit including three convolutional layers builds connections between the input and each convolutional layer. In addition, we propose a scale-related training method to improve the accuracy and robustness of crowd counting. We evaluate our method on three crowd counting benchmarks. Experimental results verify the efficiency of the proposed method, and it achieves superior performance compared with other methods.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under grant No. 61233003, in part by the Equipment Pre-research Fund under grant No. 61403120201.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, China

    Luyang Wang, Baoqun Yin, Aixin Guo, Hao Ma & Jie Cao

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  1. Luyang Wang
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  2. Baoqun Yin
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  3. Aixin Guo
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  4. Hao Ma
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  5. Jie Cao
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Correspondence to Luyang Wang.

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Wang, L., Yin, B., Guo, A. et al. Skip-connection convolutional neural network for still image crowd counting. Appl Intell 48, 3360–3371 (2018). https://doi.org/10.1007/s10489-018-1150-1

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