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Human Pose Estimation-Based ID Assignment Method in the Wild: A Real-Time Approach

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Advances in Visual Computing (ISVC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15046))

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Abstract

In the field of computer vision, research on human pose estimation aims to understand human movements by analyzing individual camera images. However, identifying individuals in multi-people pose estimation remains a challenging issue. This study explores ID assignment technology to address the technical challenge of identifying individuals and maintaining consistent identities over consecutive images. Previous studies often required additional training to perform optimally in the wild environments. Therefore, in order to address the limitation of the previous studies, we propose a novel methodology that performs real-time image analysis and evaluation without the need for any prior training or additional datasets. By identifying the number of people and analyzing individual characteristics in each frame, we compare the characteristics between the current and previous frames, assigning the same ID to the individuals showing the highest similarity. This methodology enhances the applicability of human pose estimation systems in the wild and in real-time without training. We conducted experiments across four distinct scenarios and observed that the average ID assignment accuracy across all data was approximately 86.01%. Consequently, our research is expected to make significant contributions to various applications, including human-computer interaction, sports analysis, healthcare, and surveillance systems.

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Acknowledgements

This research was supported by Culture, Sports and Tourism R & D Program through Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2022. (Project Name: Reality and virtual creation, cooperation, and participation-type variable complex space Silk Road content platform for youth, Project Number: R2022020120, Contribution Rate: 100%)

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Authors and Affiliations

  1. Korea National University of Science and Technology, Ansan, 15588, South Korea

    Pyunghwa Shin & Ohung Kwon

  2. Korea Institute of Industrial Technology, Ansan, 15588, South Korea

    Pyunghwa Shin & Ohung Kwon

Authors
  1. Pyunghwa Shin
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  2. Ohung Kwon
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Corresponding author

Correspondence to Ohung Kwon .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Johns Hopkins University, Baltimore, MD, USA

    Vishal Patel

  3. Chinese University of Hong Kong, Shatin, Hong Kong

    Jinwei Gu

  4. University of California, Davis, CA, USA

    Julian Panetta

  5. George Mason University, Fairfax, VA, USA

    Yotam Gingold

  6. University of Georgia, Athens, GA, USA

    Kyle Johnsen

  7. Colorado State University, Fort Collins, CO, USA

    Mohammed Safayet Arefin

  8. Indian Institute of Technology, Kanpur, Uttar Pradesh, India

    Soumya Dutta

  9. Los Alamos National Lab., Los Alamos, NM, USA

    Ayan Biswas

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Shin, P., Kwon, O. (2025). Human Pose Estimation-Based ID Assignment Method in the Wild: A Real-Time Approach. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2024. Lecture Notes in Computer Science, vol 15046. Springer, Cham. https://doi.org/10.1007/978-3-031-77392-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-77392-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77391-4

  • Online ISBN: 978-3-031-77392-1

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