Real-time Human Pose Estimation using RGB-D images and Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 림빈보니카 | - |
dc.contributor.author | 성낙준 | - |
dc.contributor.author | 마준 | - |
dc.contributor.author | 최유주 | - |
dc.contributor.author | 홍민 | - |
dc.date.accessioned | 2021-08-11T08:41:00Z | - |
dc.date.available | 2021-08-11T08:41:00Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1598-0170 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/3531 | - |
dc.description.abstract | Human Pose Estimation (HPE) which localizes the human body joints becomes a high potential for high-level applications in the field of computer vision. The main challenges of HPE in real-time are occlusion, illumination change and diversity of pose appearance. The single RGB image is fed into HPE framework in order to reduce the computation cost by using depth-independent device such as a common camera, webcam, or phone cam. However, HPE based on the single RGB is not able to solve the above challenges due to inherent characteristics of color or texture. On the other hand, depth information which is fed into HPE framework and detects the human body parts in 3D coordinates can be usefully used to solve the above challenges. However, the depth information-based HPE requires the depth-dependent device which has space constraint and is cost consuming. Especially, the result of depth information-based HPE is less reliable due to the requirement of pose initialization and less stabilization of frame tracking. Therefore, this paper proposes a new method of HPE which is robust in estimating self-occlusion. There are many human parts which can be occluded by other body parts. However, this paper focuses only on head self-occlusion. The new method is a combination of the RGB image-based HPE framework and the depth information-based HPE framework. We evaluated the performance of the proposed method by COCO Object Keypoint Similarity library. By taking an advantage of RGB image-based HPE method and depth information-based HPE method, our HPE method based on RGB-D achieved the mAP of 0.903 and mAR of 0.938. It proved that our method outperforms the RGB-based HPE and the depth-based HPE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국인터넷정보학회 | - |
dc.title | Real-time Human Pose Estimation using RGB-D images and Deep Learning | - |
dc.title.alternative | Real-time Human Pose Estimation using RGB-D images and Deep Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 홍민 | - |
dc.identifier.doi | 10.7472/jksii.2020.21.3.113 | - |
dc.identifier.bibliographicCitation | 인터넷정보학회논문지, v.21, no.3, pp.113 - 121 | - |
dc.relation.isPartOf | 인터넷정보학회논문지 | - |
dc.citation.title | 인터넷정보학회논문지 | - |
dc.citation.volume | 21 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 113 | - |
dc.citation.endPage | 121 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002603927 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Human pose estimation | - |
dc.subject.keywordAuthor | human skeleton tracking | - |
dc.subject.keywordAuthor | keypoint localization | - |
dc.subject.keywordAuthor | deep learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.