A review of deep learning based methods for 2D human pose estimation

Bo Yang; Hong Zhang; Xiaodong Hou; Qing Tao; Chunguang Lv; Min Zhao; Wei Li

doi:10.1117/12.3048383

3 October 2024 A review of deep learning based methods for 2D human pose estimation

Bo Yang, Hong Zhang, Xiaodong Hou, Qing Tao, Chunguang Lv, Min Zhao, Wei Li

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132722F (2024) https://doi.org/10.1117/12.3048383
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Human posture estimation is an important content in computer vision, which is the research basis of action recognition, human-computer interaction and other directions, and the traditional manual feature approach has certain problems in terms of accuracy, robustness and computation. In recent years, deep learning-based pose estimation methods have achieved high performance in human pose estimation. According to the number of people, this paper classifies deep learning-based 2D human pose estimation methods into single-person pose estimation methods and multi-person pose estimation methods, and summarizes and analyzes each method. A brief overview of the current mainstream datasets and evaluation metrics is provided. Finally, the development of 2D human pose estimation methods is summarized and possible future trends are listed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Yang, Hong Zhang, Xiaodong Hou, Qing Tao, Chunguang Lv, Min Zhao, and Wei Li "A review of deep learning based methods for 2D human pose estimation", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132722F (3 October 2024); https://doi.org/10.1117/12.3048383

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