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A Modified Pyramid Scale Network for Crowd Counting

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1613))

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Abstract

Crowd counting is a complex and strenuous task in the field of computer vision due to illumination, complex background, occlusions, scale variations, non-uniform distribution etc. There are many methods to count the crowd and the multicolumn architecture has been adopted widely to overcome those challenges. But the two major issues with multicolumn methods are feature similarity and scale limitation. To address these issues, pyramid scale network (PSNet) was proposed to deal these issues. In this paper, we propose a modified pyramid scale network (MPSNet) to obtain more detailed features to deal with scale variation. In this work, we have used four modified pyramid scale modules (MPSM) which extracts multiscale features by integrating message passing and attention mechanism in multicolumn architecture. The experiments and performance of the proposed method is compared on the benchmark ShanghaiTech dataset and UCF_CC_50 dataset in terms of mean absolute error (MAE) and root mean square error (RMSE).

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Tonk, Rajasthan, 304022, India

    Bhawana Tyagi, Swati Nigam & Rajiv Singh

Authors
  1. Bhawana Tyagi
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  2. Swati Nigam
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  3. Rajiv Singh
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Corresponding author

Correspondence to Rajiv Singh .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Tyagi, B., Nigam, S., Singh, R. (2022). A Modified Pyramid Scale Network for Crowd Counting. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1613. Springer, Cham. https://doi.org/10.1007/978-3-031-12638-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-12638-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12637-6

  • Online ISBN: 978-3-031-12638-3

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