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Structural topic model-based comparative review of human pose estimation research in the United States and China

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Abstract

Human pose estimation, a key area in computer vision, benefits various fields. The comparative study of research approaches in the United States and China, both leaders in this domain, is vital for understanding and influencing global trends in this technology. This review collected 191 influential papers from 2014 to 2022, sourced from Google Scholar. The Structural Topic Model (STM) was utilized to analyze research content, preferences, and trends in research topics. Specifically, 10 topics were summarized, and topic proportions, preferences, intensities, and word clouds were displayed via visualization methods. The findings revealed: 1) research on feature extraction and depth image constituted the largest proportion, approximately 12.2%, while data training research accounted for the lowest proportion, around 7.9%; 2) the United States and China exhibited distinct research preferences: the United States focused more on model and data research, while China emphasized deep learning and neural networks; 3) both countries exhibited similar research trends within the same topics, and research on deep learning technologies has experienced a slowdown in recent years. By comparative study, this review offers valuable insights and guidance for future investigations and applications in human pose estimation, such as improving the quality and diversity of data sets.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by the Shanghai Pujiang Program under Grant 21PJ1404000, the National Natural Science Foundation of China under Grant 62103252, the Shanghai Sports Science and Technology's "National Fitness Plan" project(22Q003), and the Shanghai Tech Rising Stars Program(22QC1401300). The authors would like to thank Jennifer (Yujiao) Qiao for the English improvement.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan DistrictShanghai, 200444, China

    Bo Sheng, Xiaohui Chen & Jing Tao

  2. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, China

    Bo Sheng

  3. Department of Exercise Sciences, The University of Auckland, 4703906, Newmarket, Auckland, New Zealand

    Yanxin Zhang

  4. Longhua Hospital, Shanghai University of TCM, No.725 South Wanping Road, Shanghai, 200032, China

    Yueli Sun

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Bo Sheng: Conceptualization, Methodology, Writing—original draft. Xiaohui Chen: Methodology, Writing—original draft. Yanxin Zhang: Writing – review & editing. Jing Tao: Writing – review & editing, Supervision. Yueli Sun: Writing – review & editing, Supervision.

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Sheng, B., Chen, X., Zhang, Y. et al. Structural topic model-based comparative review of human pose estimation research in the United States and China. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17923-0

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