The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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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“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].”
BackgroundOpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
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2D Human Pose Estimation: New Benchmark and State of the Art Analysis
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DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
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Human Action Recognition Systems: A Review of the Trends and State-of-the-Art
Karim, M. F. A., Khalid, S., Aleryani, A., Khan, J., Ullah, I., & Ali, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3373199 · Cited by 117
Mennella, C., Maniscalco, U., Pietro, G. D., & Esposito, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107485 · Cited by 65
Choi, W., Choi, T., & Heo, S. (2023) · Bioengineering · doi:10.3390/bioengineering10080891 · Cited by 28
Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone Location
Yang, J., & Park, K. (2024) · Bioengineering · doi:10.3390/bioengineering11020141 · Cited by 28
Machine Learning-Based Exercise Posture Recognition System Using MediaPipe Pose Estimation Framework
Supanich, W., Kulkarineetham, S., Sukphokha, P., & Wisarnsart, P. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10112726 · Cited by 28
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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