Review on Deep Learning-Based 2D Single-Person Pose Estimation

doi:10.3778/j.issn.1002-8331.2403-0152

Abstract

Abstract: Human pose estimation is a key technology in the field of computer vision that identifies human postures by detecting body keypoints. With the rapid advancement of deep learning, it has become the dominant approach in human pose estimation, achieving significant progress. This paper reviews single-person pose estimation research based on deep learning, examining the issue from four perspectives: data preprocessing, network architecture design, supervised learning methods, and post-processing techniques. It also explores new representations of keypoints and the application of Transformer models in this area. Additionally, the paper introduces common datasets and performance metrics, and delves into the current challenges and future directions in the field of single-person pose estimation.

Key words: two-dimensional human pose estimation, single-person pose estimation, deep learning, keypoint detection, computer vision

摘要： 人体姿态估计是计算机视觉领域的一项关键技术，它通过检测人体关键点以识别人体姿态。随着深度学习的快速发展，其已成为人体姿态估计的主流技术并取得了显著进展。围绕单人姿态估计问题，从数据预处理、网络架构设计、监督学习方法以及后处理技术四个维度对基于深度学习的单人姿态估计研究进行回顾，同时探讨关键点表征的新方式及Transformer模型在该领域的应用，此外还介绍了常用的数据集和性能估计指标，深入讨论当前单人姿态估计领域的挑战和发展方向。

关键词: 二维人体姿态估计, 单人姿态估计, 深度学习, 关键点检测, 计算机视觉

CLC Number:

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SU Yanyan, QIU Zhiliang, LI Guo, LU Shenglian, CHEN Ming. Review on Deep Learning-Based 2D Single-Person Pose Estimation[J]. Computer Engineering and Applications, 2024, 60(21): 18-37.

苏妍妍, 邱志良, 李帼, 陆声链, 陈明. 基于深度学习的二维单人姿态估计综述[J]. 计算机工程与应用, 2024, 60(21): 18-37.

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