Cited by 125Human Pose and Action RecognitionAdvanced Neural Network ApplicationsFace recognition and analysis

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Comparative Analysis of Skeleton-Based Human Pose Estimation

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Chung, Jen-Li, Ong, Lee-Yeng, Leow, Meng-Chew·2022·Future Internet

DOI 10.3390/fi14120380

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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.

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“Recently, four state ‐ of ‐ the ‐ art HPE libraries have been applied in various applications, namely PoseNet [30], MoveNet [31], OpenPose [32], and MediaPipe Pose [33].”
Background
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Authors: Chung, J.-L., Ong, L.-Y., & Leow, M.-C.
Affiliations: Multimedia University (MY)
Corresponding author: Ong, Lee-Yeng
ORCID: Ong, Lee-Yeng
Leow, Meng-Chew
Venue: Future Internet
Publisher: MDPI AG
Published: 2022-12-15
Volume: 14
Issue: 12
Pages: 380
ISSN: 1999-5903 (ISSN-L)
DOI: 10.3390/fi14120380
OpenAlex: W4313275337
Cited by: 124
References: 46
Citations by year: 2026: 21 · 2025: 38 · 2024: 33 · 2023: 32
FWCI: 8.43
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Future Internet (publishedVersion, cc-by)
Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Telekom Malaysia Research & Development (RDTC/221036 (MMUE/220003), MMUI/220001)
Multimedia University IR Fund (RDTC/221036 (MMUE/220003), MMUI/220001)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords: human pose estimation, skeleton-based pose estimation, OpenPose, PoseNet, MoveNet, MediaPipe Pose, video surveillance, medical assistance

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Comparative Analysis of Skeleton-Based Human Pose Estimation

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