TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Multi-resolution Fusion Network for Human Pose Estimation in Low-resolution Images |
| 영문제목(English Title) |
Multi-resolution Fusion Network for Human Pose Estimation in Low-resolution Images |
| 저자(Author) |
Boeun Kim
YeonSeung Choo
Hea In Jeong
Chung-Il Kim
Saim Shin
Jungho Kim
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| 원문수록처(Citation) |
VOL 16 NO. 07 PP. 2328 ~ 2344 (2022. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods. |
| 키워드(Keyword) |
Human keypoint detection
Human Pose Estimation
Low-resolution image
Small person pose estimation
2D pose estimation
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| 파일첨부 |
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