Human Activity Recognition based on Deep-Temporal Learning using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit with Features Selection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이영문 | - |
dc.date.accessioned | 2023-07-05T05:32:55Z | - |
dc.date.available | 2023-07-05T05:32:55Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112942 | - |
dc.description.abstract | Recurrent Neural Networks (RNNs) and their variants have been demonstrated tremendous successes in modeling sequential data such as audio processing, video processing, time series analysis, and text mining. Inspired by these facts, we propose human activity recognition technique to proceed visual data via utilizing convolution neural network (CNN) and Bidirectional-gated recurrent unit (Bi-GRU). Firstly, we extract deep features from frames sequence of human activities videos using CNN and then select most important features from the deep appearances to improve performance and decrease computational complexity of the model. Secondly, to learn temporal motions of frames sequence, we design Bi-GRU and feed those deep-important features extracted from frames sequence of human activities to Bi-GRU which learn temporal dynamics in forward and backward direction at each time step. We conduct extensive experiments on realistic videos of human activity recognition datasets YouTube11, HMDB51 and UCF101. Lastly, we compare the obtained results with existing methods to show the competence of our proposed technique. © 2013 IEEE. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Human Activity Recognition based on Deep-Temporal Learning using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit with Features Selection | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2023.3263155 | - |
dc.identifier.scopusid | 2-s2.0-85153046185 | - |
dc.identifier.wosid | 000967462600001 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.1, no.1, pp 1 - 12 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 1 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | bidirectional-gated recurrent unit (Bi-GRU) | - |
dc.subject.keywordAuthor | convolution neural networks (CNNs) | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | Human activity recognition | - |
dc.subject.keywordAuthor | recurrent neural networks (RNNs) | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10089162 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.