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 · 2024
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.