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Comparative Analysis of Skeleton-Based Human Pose Estimation
Open accessAbstract
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 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]
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Metadata
- Authors
- Chung, J.-L., Ong, L.-Y., & Leow, M.-C.
- Affiliations
- Multimedia University (MY)
- Corresponding author
- Ong, Lee-Yeng
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2022-12-15
- Volume
- 14
- Issue
- 12
- Pages
- 380
- ISSN
- 1999-5903 (ISSN-L)
- 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
- 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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