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A Survey on Deep Learning-Based 2D Human Pose Estimation Models

Sani Salisu1,2, A. S. A. Mohamed1,*, M. H. Jaafar3, Ainun S. B. Pauzi1, Hussain A. Younis1,4

1 School of Computer Science, Universiti Sains Malaysia, Penang, 11800, Malaysia
2 Department of Information Technology, Faculty of Computing, Federal University Dutse, Jigawa, 720211, Nigeria
3 School of Industrial Technology, Universiti Sains Malaysia, Penang, 11800, Malaysia
4 College of Education for Women, University of Basrah, Basrah, 61004, Iraqi

* Corresponding Author: A. S. A. Mohamed. Email: email

Computers, Materials & Continua 2023, 76(2), 2385-2400.


In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from the year of breakthrough are both shown in tabular form. This study intends to highlight the limitations of published reviews and surveys respecting presenting a systematic review of the current DL-based solution to the 2D HPE model. Furthermore, a detailed and meaningful survey that will guide new and existing researchers on DL-based 2D HPE models is achieved. Finally, some future research directions in the field of HPE, such as limited data on disabled persons and multi-training DL-based models, are revealed to encourage researchers and promote the growth of HPE research.


Cite This Article

S. Salisu, A. S. A. Mohamed, M. H. Jaafar, A. S. B. Pauzi and H. A. Younis, "A survey on deep learning-based 2d human pose estimation models," Computers, Materials & Continua, vol. 76, no.2, pp. 2385–2400, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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