Meta-action descriptor for action recognition in RGBD video

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...

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Bibliographic Details
Published in:IET computer vision 2017-06, Vol.11 (4), p.301-308
Main Authors: Huang, Min, Su, Song-Zhi, Cai, Guo-Rong, Zhang, Hong-Bo, Cao, Donglin, Li, Shao-Zi
Format: Article
Language:English
Subjects:
action recognition
computer vision
discrimination-based strategies
discriminative part clustering
dynamic part clustering
gesture recognition
global video features
image colour analysis
local video features
meta-action descriptor
MSR action dataset
Research Article
RGBD video
video signal processing
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Summary: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.
ISSN:1751-9632
1751-9640
1751-9640
DOI:10.1049/iet-cvi.2016.0252