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| 基于深度学习的二维人体姿态估计综述 |
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张宇1, 温光照1, 米思娅2,3, 张敏灵1,2, 耿新1
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1.东南大学 计算机科学与工程学院, 江苏 南京 211189;2.东南大学 网络空间安全学院, 江苏 南京 211189;3.网络通信与安全紫金山实验室, 江苏 南京 211111
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| 摘要: |
| 人体姿态估计是计算机视觉领域的一个基础且具有挑战的任务，人体姿态估计对于描述人体姿态、描述人体行为等至关重要，是行为识别、行为检测等计算机视觉任务的基础.近年来，随着深度学习的发展，基于深度学习的人体姿态估计算法展现出了极其优异的效果.从单人人体姿态估计、自顶向下的多人人体姿态估计和自底向上的多人人体姿态估计这3种主流的人体姿态估计方式，介绍近年来基于深度学习的二维人体姿态估计算法的发展，并讨论目前二维人体姿态估计所面临的困难和挑战.最后，对人体姿态估计未来的发展做出展望. |
| 关键词: 深度学习 二维人体姿态估计 关键点检测 |
| DOI：10.13328/j.cnki.jos.006390 |
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| 基金项目:国家重点研发计划（2018AAA0100100）；国家自然科学基金（61702095）；江苏省自然科学基金（BK20190341） |
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| Overview on 2D Human Pose Estimation Based on Deep Learning |
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ZHANG Yu1, WEN Guang-Zhao1, MI Si-Ya2,3, ZHANG Min-Ling1,2, GENG Xin1
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1.School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;2.School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China;3.Purple Mountain Laboratory, Nanjing 211111, China
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| Abstract: |
| Human pose estimation is a basic and challenging task in the field of computer vision. It is the basis for many of computer vision tasks, such as action recognition and action detection. With the development of deep learning methods, deep learning-based human pose estimation algorithms have shown excellent results. This study divides pose estimation methods into three categories, including single person pose estimation, top-down multi-person pose estimation, and bottom-up multi-person pose estimation. The development of 2D human pose estimation algorithms in recent years is introduced, and the current challenges of two-dimensional human pose estimation are discussed. Finally, the outlook for the future development of human pose estimation is given. |
| Key words: deep learning 2D human pose estimation keypoint detection |
