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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 2×
“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
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,395
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.471 · Cited by 2,699
Simple Baselines for Human Pose Estimation and Tracking
Xiao, B., Wu, H., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_29 · Cited by 2,278
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.533 · Cited by 1,090
Towards Accurate Multi-person Pose Estimation in the Wild
Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., & Murphy, K. P. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.395 · Cited by 917
Pose-Driven Deep Convolutional Model for Person Re-identification
Su, C., Li, J., Zhang, S. S., Xing, J., Gao, W., & Tian, Q. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.427 · Cited by 850
Chang, Y., Chen, S.-F., & Huang, J.-D. (2011) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2011.07.002 · Cited by 754
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00539 · Cited by 560
Attention-Aware Compositional Network for Person Re-identification
Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00226 · Cited by 517
BlazePose: On-device Real-time Body Pose tracking
Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., & Grundmann, M. (2020) · arXiv · doi:10.48550/arxiv.2006.10204 · Cited by 475
From Actemes to Action: A Strongly-Supervised Representation for Detailed Action Understanding
Zhang, W., Zhu, M., & Derpanis, K. G. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.280 · Cited by 368
Zhou, X., Wang, D., & Krähenbühl, P. (2019) · arXiv · doi:10.48550/arxiv.1904.07850 · Cited by 288
Fall Detection Based on Key Points of Human-Skeleton Using OpenPose
Chen, W., Jiang, Z., Guo, H., & Ni, X. (2020) · Symmetry · doi:10.3390/sym12050744 · Cited by 234
UniPose: Unified Human Pose Estimation in Single Images and Videos
Artacho, B., & Savakis, A. E. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00706 · Cited by 205
Ergonomic postural assessment using a new open-source human pose estimation technology (OpenPose)
Kim, W., Sung, J., Saakes, D. P., Huang, C., & Xiong, S. (2021) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2021.103164 · Cited by 192
Combining Detection and Tracking for Human Pose Estimation in Videos
Wang, M., Tighe, J., & Modolo, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01110 · Cited by 145
AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance
Wang, J., Qiu, K., Peng, H., Fu, J., & Zhu, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350910 · Cited by 143
Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices
Jo, B., & Kim, S. (2022) · Traitement du signal · doi:10.18280/ts.390111 · Cited by 100
Deep Learning-Based Human Pose Estimation: A SurveyPreprint
Zheng, C., Wu, W., Chen, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.13392 · Cited by 84
Human pose estimation for mitigating false negatives in weapon detection in video-surveillance
Lamas, A., Tabik, S., Montes, A., Pérez-Hernández, F., García, J., Olmos, R., & Herrera, F. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.059 · Cited by 79
Mixture Dense Regression for Object Detection and Human Pose Estimation
Varamesh, A., & Tuytelaars, T. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01310 · Cited by 50
Prediction of Basketball Free Throw Shooting by OpenPose
Nakai, M., Tsunoda, Y., Hayashi, H., & Murakoshi, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31605-1_31 · Cited by 49
Deep Motion Analysis for Epileptic Seizure Classification
Ahmedt‐Aristizabal, D., Nguyen, K., Denman, S., Sridharan, S., Dionisio, S. A., & Fookes, C. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513031 · Cited by 41
Emerging Research in Electronics, Computer Science and Technology
(2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-5802-9 · Cited by 37
Telehealth Using PoseNet-Based System for In-Home Rehabilitation
Chua, J., Ong, L.-Y., & Leow, M.-C. (2021) · Future Internet · doi:10.3390/fi13070173 · Cited by 36
Self-citationPrediction of Volleyball Trajectory Using Skeletal Motions of Setter Player
Shuya, S., Makino, Y., & Shinoda, H. (2019) · Augmented Human International Conference · doi:10.1145/3311823.3311844 · Cited by 33
Intelligent Fitness Trainer System Based on Human Pose Estimation
Zou, J., Li, B., Wang, L., Li, Y., Li, X., Lei, R., & Sun, S. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-7123-3_69 · Cited by 31
Baseball Swing Pose Estimation Using OpenPose
Li, Y.-C., Chang, C.-T., Cheng, C.-C., & Huang, Y.-L. (2021) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai52226.2021.9507807 · Cited by 30
Cricket Shot Image Classification Using Random Forest
Devanandan, M., Rasaratnam, V., Anbalagan, M. K., Asokan, N., Panchendrarajan, R., & Tharmaseelan, J. (2021) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac54203.2021.9671109 · Cited by 30
Park, H.-J., Baek, J.-W., & Kim, J.-H. (2020) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case48305.2020.9216833 · Cited by 26
Body Posture Detection and Motion Tracking using AI for Medical Exercises and Recommendation System
Patil, A., Rao, D., Utturwar, K., Shelke, T., & Sarda, E. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224403043 · Cited by 26
Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population
Shapoval, S., García-Zapirain, B., Zorrilla, A. M., & Mugueta-Aguinaga, I. (2021) · Applied Sciences · doi:10.3390/app11083502 · Cited by 19
Light-Weight Seated Posture Guidance System with Machine Learning and Computer Vision
Kapoor, R., Jaiswal, A., & Makedon, F. S. (2022) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3529190.3535341 · Cited by 18
Sentimental Analysis and Deep Learning
(2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5157-1 · Cited by 18
FitMe: A Fitness Application for Accurate Pose Estimation Using Deep Learning
Agarwal, S., Gupta, M., Khandelwal, S., Jain, P., Aggarwal, A., Singh, D., & Mishra, V. K. (2021) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc51823.2021.9478168 · Cited by 16
Lightweight 2D Human Pose Estimation for Fitness Coaching System
Jeon, H., Yoon, Y., & Kim, D. (2021) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · doi:10.1109/itc-cscc52171.2021.9501458 · Cited by 16
Normalized Human Pose Features for Human Action Video Alignment
Liu, J., Shi, M., Chen, Q., Fu, H., & Tai, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01132 · Cited by 16
Anomaly Detection in Surveillance Video Using Pose Estimation
Thyagarajmurthy, A., Ninad, M. G., Rakesh, B. G., Niranjan, S. K., & Manvi, B. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-5802-9_66 · Cited by 15
Scoring of Human Body-Balance Ability on Wobble Board Based on the Geometric Solution
Nguyen, H. T. P., Woo, Y., Huynh, N. N., & Jeong, H. (2022) · Applied Sciences · doi:10.3390/app12125967 · Cited by 14
Automatic Feedback For Physiotherapy Exercises Based On PoseNet
رکبه, ح., عبدالله, ب. ح., عبدالله, ا. ا., عبدالعال, ر. ا., نعمان, ر. ر., درویش, ا. خ., & البهیدى, و. (2020) · النشرة المعلوماتية في الحاسبات والمعلومات · doi:10.21608/fcihib.2020.116046 · Cited by 11
Gadhiya, R., & Kalani, N. B. (2021) · International Conference on Circuits, Controls and Communications (CCUBE) · doi:10.1109/ccube53681.2021.9702726 · Cited by 11
A Kidnapping Detection Using Human Pose Estimation in Intelligent Video Surveillance Systems
Park, J. H., Song, K., & Kim, Y. S. (2018) · Journal of the Korea Society of Computer and Information · doi:10.9708/jksci.2018.23.08.009 · Cited by 8
You, Y., & Zhao, Y. (2019) · Personal and Ubiquitous Computing · doi:10.1007/s00779-019-01255-8 · Cited by 8
BeFit—A Real-Time Workout Analyzer
Joseph, R., Ayyappan, M., Shetty, T., Gaonkar, G., & Nagpal, A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5157-1_24 · Cited by 8
Elcare: Elderly Care With Fall Detection
Jawale, C. D., Joshi, K. A., Gogate, S. K., & Badgujar, C. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2273/1/012019 · Cited by 7
Deep Learning for FitnessPreprint
Mahendran, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01376 · Cited by 4
An OpenPose-based Child Abuse Decision System using Surveillance Video
Yoo, H.-R., & Lee, B.-H. (2019) · The Journal of the Korean Institute of Information and Communication Engineering · doi:10.6109/jkiice.2019.23.3.282 · Cited by 2
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
