임베디드 시스템을 위한 LSTM-RNN을 이용한 Skeleton 기반 동적 제스처 인식
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, S | - |
dc.contributor.author | KIM, WHOI YUL | - |
dc.date.accessioned | 2021-08-02T12:51:09Z | - |
dc.date.available | 2021-08-02T12:51:09Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2018-11 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/15885 | - |
dc.description.abstract | In our work, dynamic gestures in SHREC’17 public database are recognized by extracting simple features from the coordinates of the hand skeleton and using a LSTM-RNN. By using our method, the higher recognition performance is acquired than the existing methods even if a simple structure of LSTM-RNN is used. The trained LSTM-RNN structure can be implemented to a embedded board because of its simplicity and small size. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한전자공학회 | - |
dc.title | 임베디드 시스템을 위한 LSTM-RNN을 이용한 Skeleton 기반 동적 제스처 인식 | - |
dc.title.alternative | Skeleton-Based Dynamic Gesture Recognition Using LSTM-RNN for Embbeded System | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | KIM, WHOI YUL | - |
dc.identifier.bibliographicCitation | 2018 대한전자공학회 추계학술대회, pp.806 - 808 | - |
dc.relation.isPartOf | 2018 대한전자공학회 추계학술대회 | - |
dc.citation.title | 2018 대한전자공학회 추계학술대회 | - |
dc.citation.startPage | 806 | - |
dc.citation.endPage | 808 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceeding | - |
dc.description.journalClass | 3 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.identifier.url | https://www.dbpia.co.kr/pdf/pdfView.do?nodeId=NODE07625014 | - |
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