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Comparative Analysis of Skeleton-Based Human Pose Estimation

Author

Listed:
  • Jen-Li Chung

    (Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia)

  • Lee-Yeng Ong

    (Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia)

  • Meng-Chew Leow

    (Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia)

Abstract

Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.

Suggested Citation

  • Jen-Li Chung & Lee-Yeng Ong & Meng-Chew Leow, 2022. "Comparative Analysis of Skeleton-Based Human Pose Estimation," Future Internet, MDPI, vol. 14(12), pages 1-19, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:380-:d:1005029
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    Cited by:

    1. Damián Keller & Azeema Yaseen & Joseph Timoney & Sutirtha Chakraborty & Victor Lazzarini, 2023. "Banging Interaction: A Ubimus-Design Strategy for the Musical Internet," Future Internet, MDPI, vol. 15(4), pages 1-17, March.
    2. Manuel J. C. S. Reis, 2023. "Developments of Computer Vision and Image Processing: Methodologies and Applications," Future Internet, MDPI, vol. 15(7), pages 1-3, June.

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