计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 219-228.doi: 10.11896/jsjkx.210900041

• 计算机图形学&多媒体 • 上一篇    下一篇

深度学习方法在二维人体姿态估计的研究进展

张国平1,3, 马楠2, 贯怀光1, 吴祉璇1   

  1. 1 北京联合大学北京市信息服务工程重点实验室 北京100101
    2 北京工业大学信息学部 北京100124
    3 北京联合大学机器人学院 北京100101
  • 收稿日期:2021-09-06 修回日期:2022-01-30 发布日期:2022-12-14
  • 通讯作者: 马楠(manan123@bjut.edu.cn)
  • 作者简介:(diffzhang@163.com)
  • 基金资助:
    国家自然科学基金(61871038,61931012)

Research Progress of Deep Learning Methods in Two-dimensional Human Pose Estimation

ZHANG Guo-ping1,3, MA Nan2, Guan Huai-guang1, WU Zhi-xuan1   

  1. 1 Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China
    2 Department of Information Science,Beijing University of Technology,Beijing 100124,China
    3 College of Robotics,Beijing Union University,Beijing 100101,China
  • Received:2021-09-06 Revised:2022-01-30 Published:2022-12-14
  • About author:ZHANG Guo-ping,born in 1995,master.His main research interests include human pose estimation,interactive cognition and action recognition.MA Nan,born in 1978,Ph.D,professor.Her main research interests include interactive cognition,intelligent driving,knowledge discovery and intelligent system.
  • Supported by:
    National Natural Science Foundation of China(61871038,61931012).

摘要: 人体姿态估计的任务是对图像或视频中的人体关键点进行定位和检测,其一直是计算机视觉领域的热点研究方向之一,也是计算机理解人类行为动作的关键一步。近年来,图像和视频中的二维人体姿态关键点预测在许多领域有着广泛的应用,二维人体姿态估计利用深度学习强大的图像特征提取能力,提升了其鲁棒性、准确性并缩短了处理时间,而且表现效果远超传统方法。根据二维人体姿态研究对象数量的不同,可将其分为单人以及多人姿态估计方法。针对单人姿态估计,根据提取到的关键点表示的不同,可采用基于直接预测人体坐标点的坐标回归方法,以及预测人体关键点高斯分布的基于热图的检测方法;针对多人姿态估计,可采用的方法分为解决多人到单人过程的自顶向下方法,以及直接处理多人关键点的自底向上方法。根据现有的人体姿态估计方法对其进行总结,说明网络结构的内部机制及执行过程,并对常用的数据集、评价指标进行分析,最后阐述当前面临的问题及未来发展趋势。

关键词: 二维人体姿态估计, 深度学习, 单人姿态估计, 多人姿态估计, 评价指标

Abstract: The task of human pose estimation is to locate and detect the key points of human body in images or videos.It has always been one of the hot research directions in the field of computer vision,and it is also a key step for computers to understand human actions.In recent years,it has wide application for predicting the poses of two-dimensional human body key points in images and videos.Using the powerful image feature extraction capabilities of deep learning,two-dimensional human pose estimation has been improved in robustness,accuracy,and processing time,and the performance effect is far beyond traditional methods.According to the different number of objects in the two-dimensional human body pose,it can be divided into single-person and multi-person pose estimation methods.For single-person pose estimation,according to the different representations of the extracted key points,coordinate regression methods based on the direct prediction of human coordinate points and heat map detection methods based on predicting the Gaussian distribution of human key points can be used.In multi-person pose estimation,it is divided into the top-down method which solves the process from multiple people to a single person,and a bottom-up method that directly deals with the key points of multiple people.Based on the existing estimation methods of human body posture,this paper analyzes the internal mechanism of the network structure,analyzes the commonly used datasets and evaluation indicators,and elaborates the current problems and future development trends.

Key words: Two-dimensional human pose estimation, Deep learning, Single-person pose estimation, Multi-person pose estimation, Evaluation metrics

中图分类号: 

  • TP391
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