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Dense crowd counting based on adaptive scene division

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Abstract

With the rapid development of computer vision and artificial intelligence, crowd counting has attracted significant attention from researchers and many well-known methods were proposed. However, due to interocclusions, perspective distortion, and uneven crowd distribution, crowd counting is still a highly challenging task in crowd analysis. Motivated by granular computing, a novel end-to-end crowd counting network (GrCNet) is proposed to enable the problem of crowd counting to be conceptualized at different levels of granularity, and to map problem into computationally tractable subproblems. It shows that by adaptively dividing the image into granules and then feeding the granules into different counting subnetworks separately, the scale variation range of image is narrowed and the the adaptability of counting algorithm to different scenarios is improved. Experiments on four well-known crowd counting benchmark datasets indicate that GrCNet achieves state-of-the-art counting performance and high robustness in dense crowd counting.

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Acknowledgements

The authors would like to thank the editors for their kindly help and the anonymous referees for their valuable comments and helpful suggestions. The work is partially supported by the National Natural Science Foundation of China (Serial No. 61563016, 61762036), the Natural Science Foundation of Jiangxi Provincial (Serial No. 20181BAB202023, 20171BAB202012).

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Authors and Affiliations

  1. College of Software, East China Jiaotong University, Nanchang, 330013, China

    Ying Yu, Huilin Zhu & Lewei Wang

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2G7, Canada

    Witold Pedrycz

  3. System Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

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  1. Ying Yu
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  2. Huilin Zhu
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  3. Lewei Wang
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  4. Witold Pedrycz
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Correspondence to Ying Yu.

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Yu, Y., Zhu, H., Wang, L. et al. Dense crowd counting based on adaptive scene division. Int. J. Mach. Learn. & Cyber. 12, 931–942 (2021). https://doi.org/10.1007/s13042-020-01212-5

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  • DOI: https://doi.org/10.1007/s13042-020-01212-5

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