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International Conference on Image and Graphics

ICIG 2021: Image and Graphics pp 222–234Cite as

Illumination-Enhanced Crowd Counting Based on IC-Net in Low Lighting Conditions

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

Abstract

The low lighting in some extreme conditions always affect the accuracy of the crowd counting and other vision tasks. The existing methods mainly rely on the generalization ability of deep-learning model to count the crowd number. But in extremely low lighting conditions, these methods are not efficient. To alleviate this issue, this paper proposes a novel approach, named Illumination-aware Cascading Network (IC-Net). The IC-Net can handle the low lighting conditions and generate a high-quality crowd density map. It contains two submodules, i.e., the Illumination Fusion Module and the Feature Cascading Module. The Illumination Fusion Module can fuse the low-illumination feature and the illumination enhanced feature to highlight the missing feature in darkness. The Feature Cascading Module is a cascading model and used to further express the illumination feature. It can generate the high-quality density map. In addition, a new dataset is collected, named Low Light Scenes Crowd (LLSC) dataset, which all come from extremely low illumination conditions. Experimental results on LLSC and benchmark show that the proposed method outperforms the existing state-of-the art methods in such extreme conditions.

Supported by the National Natural Science Foundation of China (Grant No. 62076117 and No. 61762061), the Natural Science Foundation of Jiangxi Province, China (Grant No. 20161ACB20004) and Jiangxi Key Laboratory of Smart City (Grant No. 20192BCD40002).

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Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Haoyu Zhao & Yi Zou

  2. School of Software, Nanchang University, Nanchang, 330047, China

    Weidong Min

  3. Jiangxi Key Laboratory of Smart City, Nanchang, 330047, China

    Weidong Min

Authors
  1. Haoyu Zhao
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  2. Weidong Min
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  3. Yi Zou
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Corresponding author

Correspondence to Weidong Min .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Cite this paper

Zhao, H., Min, W., Zou, Y. (2021). Illumination-Enhanced Crowd Counting Based on IC-Net in Low Lighting Conditions. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-87355-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87354-7

  • Online ISBN: 978-3-030-87355-4

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