Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Artículo Open Access |
| Published: |
Wiley
2024
|
| Subjects: | |
| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/cav.2244 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1867002193970724865 |
|---|---|
| author | Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu |
| author_facet | Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu |
| collection | Wiley Open Access |
| contents | Hybrid attention adaptive sampling network for human pose estimation in videos Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu Computer Animation and Virtual Worlds AbstractHuman pose estimation in videos often uses sampling strategies like sparse uniform sampling and keyframe selection. Sparse uniform sampling can miss spatial‐temporal relationships, while keyframe selection using CNNs struggles to fully capture these relationships and is costly. Neither strategy ensures the reliability of pose data from single‐frame estimators. To address these issues, this article proposes an efficient and effective hybrid attention adaptive sampling network. This network includes a dynamic attention module and a pose quality attention module, which comprehensively consider the dynamic information and the quality of pose data. Additionally, the network improves efficiency through compact uniform sampling and parallel mechanism of multi‐head self‐attention. Our network is compatible with various video‐based pose estimation frameworks and demonstrates greater robustness in high degree of occlusion, motion blur, and illumination changes, achieving state‐of‐the‐art performance on Sub‐JHMDB dataset. 10.1002/cav.2244 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/cav.2244 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_cav_2244 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Hybrid attention adaptive sampling network for human pose estimation in videos Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu Computer Animation and Virtual Worlds Hybrid attention adaptive sampling network for human pose estimation in videos Qianyun Song Hao Zhang Yanan Liu Shouzheng Sun Dan Xu Computer Animation and Virtual Worlds AbstractHuman pose estimation in videos often uses sampling strategies like sparse uniform sampling and keyframe selection. Sparse uniform sampling can miss spatial‐temporal relationships, while keyframe selection using CNNs struggles to fully capture these relationships and is costly. Neither strategy ensures the reliability of pose data from single‐frame estimators. To address these issues, this article proposes an efficient and effective hybrid attention adaptive sampling network. This network includes a dynamic attention module and a pose quality attention module, which comprehensively consider the dynamic information and the quality of pose data. Additionally, the network improves efficiency through compact uniform sampling and parallel mechanism of multi‐head self‐attention. Our network is compatible with various video‐based pose estimation frameworks and demonstrates greater robustness in high degree of occlusion, motion blur, and illumination changes, achieving state‐of‐the‐art performance on Sub‐JHMDB dataset. 10.1002/cav.2244 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Hybrid attention adaptive sampling network for human pose estimation in videos |
| topic | Computer Animation and Virtual Worlds |
| url | https://onlinelibrary.wiley.com/doi/10.1002/cav.2244 |