PAM-based flexible generative topic model for 3D interactive activity recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huynh-The T. | - |
dc.contributor.author | Banos O. | - |
dc.contributor.author | Le B.-V. | - |
dc.contributor.author | Bui D.-M. | - |
dc.contributor.author | Lee S. | - |
dc.contributor.author | Yoon Y. | - |
dc.contributor.author | Le-Tien T. | - |
dc.date.available | 2021-02-22T11:30:18Z | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 2162-1020 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/9983 | - |
dc.description.abstract | Interactive activity recognition from the RGB videos still remains a challenge, therefore some existing approaches paid the attention to RGB-Depth video process to avoid problems relating to mutual occlusion and redundant human pose and to improve accuracy of skeleton extraction. From the single action to complex interaction activity, it is necessary an efficient model to describe the relationship of body components between multi-human objects. In this research, the authors proposed a hierarchical model based on the Pachinko Allocation Model for interaction recognition. Concretely, the joint features comprising joint distant and joint motion are calculated from the skeleton position and then support to topic modeling. The probabilistic models describing the flexible relationship between features - poselets - activities are generated by this model. Finally, the Binary Tree of Support Vector Machine is applied for classification. Compared with existing state-of-the-arts, the proposed method outperforms in overall classification accuracy (8-21% approximately) with the SBU Kinect Interaction Dataset. © 2015 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | PAM-based flexible generative topic model for 3D interactive activity recognition | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ATC.2015.7388302 | - |
dc.identifier.scopusid | 2-s2.0-84961775224 | - |
dc.identifier.bibliographicCitation | 2015 International Conference on Advanced Technologies for Communications (ATC), v.2016-January, pp 117 - 122 | - |
dc.citation.title | 2015 International Conference on Advanced Technologies for Communications (ATC) | - |
dc.citation.volume | 2016-January | - |
dc.citation.startPage | 117 | - |
dc.citation.endPage | 122 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Arts computing | - |
dc.subject.keywordPlus | Binary trees | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordPlus | Correlation methods | - |
dc.subject.keywordPlus | Extraction | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Hierarchical systems | - |
dc.subject.keywordPlus | Musculoskeletal system | - |
dc.subject.keywordPlus | Pattern recognition | - |
dc.subject.keywordPlus | Support vector machines | - |
dc.subject.keywordPlus | Classification accuracy | - |
dc.subject.keywordPlus | Computational model | - |
dc.subject.keywordPlus | Interaction activities | - |
dc.subject.keywordPlus | Interactive activities | - |
dc.subject.keywordPlus | Pachinko allocation models | - |
dc.subject.keywordPlus | Resource management | - |
dc.subject.keywordPlus | Skeleton | - |
dc.subject.keywordPlus | Videos | - |
dc.subject.keywordPlus | 3D modeling | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Correlation | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Resource management | - |
dc.subject.keywordAuthor | Skeleton | - |
dc.subject.keywordAuthor | Support vector machines | - |
dc.subject.keywordAuthor | Videos | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7388302 | - |
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