TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Multi-Stage Convolution Machine with Scaling and Dilation for Human Pose Estimation |
| 영문제목(English Title) |
A Multi-Stage Convolution Machine with Scaling and Dilation for Human Pose Estimation |
| 저자(Author) |
Yali Nie
Jaehwan Lee
Sook Yoon
Dong Sun Park
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| 원문수록처(Citation) |
VOL 13 NO. 06 PP. 3182 ~ 3198 (2019. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.
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| 키워드(Keyword) |
CNN
Human pose estimation
Multi-stage
Pyramid stacking
Dilation
Gating
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| 파일첨부 |
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