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PAM-based flexible generative topic model for 3D interactive activity recognition

Authors
Huynh-The, ThienBanos, OrestiLe, Ba-VuiBui, Dinh-MaoLee, SungyoungYoon, YongikLe-Tien, Thuong
Issue Date
Oct-2015
Publisher
IEEE Computer Society
Keywords
Computational modeling; Correlation; Feature extraction; Resource management; Skeleton; Support vector machines; Videos
Citation
2015 International Conference on Advanced Technologies for Communications (ATC), v.2016-January, pp 117 - 122
Pages
6
Journal Title
2015 International Conference on Advanced Technologies for Communications (ATC)
Volume
2016-January
Start Page
117
End Page
122
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/9983
DOI
10.1109/ATC.2015.7388302
ISSN
2162-1020
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.
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공과대학 (인공지능공학부)
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