.png){kind=icon}
返回
Human pose estimation using deep learning: review, methodologies, progress and future research directions
.png){kind=icon}
PRE
AI
DOI：10.1007/s13735-022-00261-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Human pose estimation (HPE) has developed over the past decade into a vibrant field for research with a variety of real-world applications like 3D reconstruction, virtual testing and re-identification of the person. Information about human poses is also a critical component in many downstream tasks, such as activity recognition and movement tracking. This review focuses on the key aspects of deep learning in the development of both 2D & 3D HPE. It provides detailed information on the variety of databases, performance metrics and human body models incorporated for implementing HPE methodologies. This paper discusses variety of applications of HPE across domains like activity recognition, animation and gaming, virtual reality, video tracking, etc. The paper presents an analytical study of all the major works that use deep learning methods for various downstream tasks in each domain for both 2D & 3D HPE. Finally, it discusses issues and limitations in the current topic of HPE and recommend potential future research directions in order to make meaningful progress in this area.
Keyword:
Human pose estimation
Action recognition
Deep learning
期刊
IF:
2.9
论文数:
280
被引数:
866
机构
引用论文
Effects of heated seat and foot heater on thermal comfort and heater energy consumption in vehicle加热座椅和脚加热器对车内热舒适性和加热器能耗的影响.png)
Ergonomics
ErgonomicsIF0
PREAI
分享收藏
查看更多内容