Action Recognition Using Frame Average Feature Map with 2D Convolutional Neural Network for Real-Time Video Analysis.
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, K. | - |
dc.contributor.author | Park, H. | - |
dc.contributor.author | Shin, J. | - |
dc.contributor.author | Ha, J. | - |
dc.contributor.author | Paik, J. | - |
dc.date.accessioned | 2021-05-20T07:40:46Z | - |
dc.date.available | 2021-05-20T07:40:46Z | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44020 | - |
dc.description.abstract | A typical video action recognition system has a high computational cost and is not suitable for real-time applications. To solve this problem, we propose an action recognition method using a two-dimensional convolutional neural network (2D CNN), which has a significantly lower computational cost than a 3D CNN. In addition, the proposed method uses a small number of frames from the video for an accurate result. The proposed method consists of: i) pretrained VGG16,which is a2D CNN, to train the action of the video andii) a test with an average of ten frames per dataset. The proposed method improved the recognition performance with a reduce computational cost by using the average of several frames instead of directly analyzing all the frames for real-time video analysis environments. © 2020 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Action Recognition Using Frame Average Feature Map with 2D Convolutional Neural Network for Real-Time Video Analysis. | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICCE-Asia49877.2020.9277163 | - |
dc.identifier.bibliographicCitation | 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85098891745 | - |
dc.citation.title | 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Cost benefit analysis | - |
dc.subject.keywordPlus | Statistical tests | - |
dc.subject.keywordPlus | Action recognition | - |
dc.subject.keywordPlus | Action recognition systems | - |
dc.subject.keywordPlus | Computational costs | - |
dc.subject.keywordPlus | Feature map | - |
dc.subject.keywordPlus | Real-time application | - |
dc.subject.keywordPlus | Real-time video analysis | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.description.journalRegisteredClass | scopus | - |
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