Jian Lu, Tengfei Yang, Bo Zhao, Hangying Wang, Maoxin Luo, Yanran Zhou, Zhe Li. Review of Deep Learning-Based Human Pose Estimation[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2400005
- Laser & Optoelectronics Progress
- Vol. 58, Issue 24, 2400005 (2021)
![Temporal pose estimation network[18]](/richHtml/lop/2021/58/24/2400005/img_1.jpg)
Fig. 1. Temporal pose estimation network[18]
![CNN structure of detection-following-regression[19]](/richHtml/lop/2021/58/24/2400005/img_2.jpg)
Fig. 2. CNN structure of detection-following-regression[19]
Fig. 3. Logarithmic polarization grouping
Fig. 4. Multi-context attention mechanism framework[22]
Fig. 5. Framework of generative adversarial networks[26]
Fig. 6. Comparison of the results with and without human structure prior during network training[27]
Fig. 7. Adversarial PoseNet model structure[27]
Fig. 8. PRM structure[28]
Fig. 9. Multi-scale structure-aware network[11]
Fig. 10. Specific process of joint human body association[44]
Fig. 11. Schematic of dilated convolution
Fig. 12. CPN overall structure[46]
Fig. 13. Comparison of experimental results of various factors affecting the performance of multi-person pose estimation[46]
Fig. 14. Human detection problem[47]
Fig. 15. Pose estimation process of SPLP network[38]
Fig. 16. Two-branch multi-stage CNN architecture[48]
Fig. 17. Schematic of PAFs method[48]
Fig. 18. Comparison results of single person pose estimation experiments (LSP)
Fig. 19. Comparison results of single person pose estimation experiments (MPII)
Fig. 20. Comparison results of multi-person pose estimation experiments (MPII)
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Table 1. Information of domestic and foreign datasets
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