ADD-Net: Attention Based 3D Dense Network for Action Recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Qiaoyue Man | - |
dc.contributor.author | 조영임 | - |
dc.date.available | 2020-02-27T06:41:21Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.issn | 1598-849X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/2457 | - |
dc.description.abstract | Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국컴퓨터정보학회 | - |
dc.relation.isPartOf | 한국컴퓨터정보학회논문지 | - |
dc.title | ADD-Net: Attention Based 3D Dense Network for Action Recognition | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.bibliographicCitation | 한국컴퓨터정보학회논문지, v.24, no.6, pp.21 - 28 | - |
dc.identifier.kciid | ART002474594 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 28 | - |
dc.citation.startPage | 21 | - |
dc.citation.title | 한국컴퓨터정보학회논문지 | - |
dc.citation.volume | 24 | - |
dc.citation.number | 6 | - |
dc.contributor.affiliatedAuthor | Qiaoyue Man | - |
dc.contributor.affiliatedAuthor | 조영임 | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Action Recognition | - |
dc.subject.keywordAuthor | Convolution Neural Network | - |
dc.subject.keywordAuthor | Attention Mechanism | - |
dc.description.journalRegisteredClass | kci | - |
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