Comparative Analysis of Skeleton-Based Human Pose Estimation

Chung, Jen-Li; Ong, Lee-Yeng; Leow, Meng-Chew

doi:10.3390/fi14120380

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

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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Standard pose estimation frameworks, such as OpenPose or MediaPipe, frequently suffer from tracking degradation in high-velocity, non-linear scenarios, resulting in lost keypoints and identity switching.
“Standard pose estimation frameworks, such as OpenPose or MediaPipe, frequently suffer from tracking degradation in these high-velocity, non-linear scenarios, resulting in lost keypoints and identity switching [5,6].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [5]
The Discussion (p. 15) reports that OpenPose was weak at detecting keypoints in continuous video frames, and the Image Dataset results (p. 8) report poor keypoint-detection performance for MediaPipe Pose. But the Discussion says MediaPipe Pose performed well on video, and the cited passages do not establish frequent degradation in high-velocity, non-linear motion or identity switching. (LLM, conf=0.86)
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[14]14. Landi, H. Google, ProMedica Team up with IncludeHealth to Tap into Growing Virtual MSK Market. Fierce Healthcare. Available online: https://www.fiercehealthcare.com/tech/google-promedica-team-up-includehealth-to-tap-into-virtual-msk-market (accessed on 16 July 2022). strengths and weaknesses in processing image and video HPE. This study focused only on 2D single-person HPE. PDJ was the evaluation metric used in this study to evaluate the quality of the HPE libraries. As a result, MoveNet has superior performance while MediaPipe Pose has the lowest performance in detecting images. In addition, MoveNet also has top performance while OpenPose has the lowest performance in detecting videos. However, OpenPose has the second highest performance in detecting images. PoseNet showed average performance in detecting images and videos. When facing challenges such as inappropriate camera position or self-occlusion, the performance in detecting body parts will be reduced. MoveNet, MediaPipe Pose, and PoseNet can handle these challenges well, but OpenPose shows the poorest performance under these conditions. In detecting videos, OpenPose had the lowest robustness because it loses track when self-occlusion occurs with body parts. The limitation of this study is that the experiments were focused on analyzing the performance of four HPE libraries using the PDJ. The memory consumption, inference time, and detection speed of each HPE library will be compared in our future work. Author Contributions: Funding acquisition, L.-Y.O.; Investigation, J.-L.C.; Project administration, L.-Y.O.; Supervision, L.-Y.O.; Visualization, J.-L.C.; Writing-original draft, J.-L.C.; Writing-review & editing, L.-Y.O. and M.-C.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Telekom Malaysia Research & Development, RDTC/221036 (MMUE/220003) and Multimedia University IR Fund, MMUI/220001. Data Availability Statement: Not Applicable, the study does not report any data. Conflicts of Interest: The authors declare no conflict of interest.

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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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