NDLI: Meta-action descriptor for action recognition in RGBD video

Content Provider	IET Digital Library
Author	Huang, Min Su, Song Zhi Cai, Guo Rong Zhang, Hong Bo Cao, Donglin Li, Shao Zi
Abstract	Action recognition is one of the hottest research topics in computer vision. Recent methods represent actions based on global or local video features. These approaches, however, lack semantic structure and may not provide a deep insight into the essence of an action. In this work, the authors argue that semantic clues, such as joint positions and part-level motion clustering, help verify actions. To this end, a meta-action descriptor for action recognition in RGBD video is proposed in this study. Specifically, two discrimination-based strategies – dynamic and discriminative part clustering – are introduced to improve accuracy. Experiments conducted on the MSR Action 3D dataset show that the proposed method significantly outperforms the methods without joint position semantic.
Starting Page	301
Ending Page	308
Page Count	8
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 4, Jun (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0252
Journal	IET Computer Vision
Publisher Date	2016-12-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Action Recognition Computer Vision Computer Vision And Image Processing Technique Discrimination-based Strategy Discriminative Part Clustering Dynamic Part Clustering Gesture Recognition Global Video Feature Image Colour Analysis Image Recognition Local Video Feature Meta-action Descriptor MSR Action Dataset RGBD Video Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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