Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2020-01-21T07:09:06Z | - |
dc.date.available | 2020-01-21T07:09:06Z | - |
dc.date.issued | 2019-11 | - |
dc.identifier.citation | 2019년도 대한전자공학회 추계학술대회 논문집, Page. 431-435 | en_US |
dc.identifier.uri | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09282279&language=ko_KR | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/122150 | - |
dc.description.abstract | 3D pose estimation is a study of estimating human 3D joints from a single image, and it is widely used in industrial fields and applications. The performance of 3D pose estimation has dramatically improved with the deep learning. However, the lack of 3D data has always been a constant problem. To solve this issue, we propose multi-stage learning method that uses both 2D and 3D datasets. We achieved 92.0% accuracy with Human3.6M dataset and obtained natural 3D pose results on outdoor images. | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 대한전자공학회 | en_US |
dc.title | 단계적 딥러닝 네트워크 학습 방법을 통한 3차원 관절 좌표 추정 | en_US |
dc.title.alternative | Deep Learning Network Two-Stage Learning Method for 3D Pose Estimation | en_US |
dc.type | Article | en_US |
dc.relation.page | 1-5 | - |
dc.contributor.googleauthor | 조용채 | - |
dc.contributor.googleauthor | 한정훈 | - |
dc.contributor.googleauthor | 이호경 | - |
dc.contributor.googleauthor | 문영식 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DIVISION OF COMPUTER SCIENCE | - |
dc.identifier.pid | ysmoon | - |
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