NDLI: Efficient human motion capture data annotation via multi-view spatiotemporal feature fusion

Content Provider	IET Digital Library
Author	Liu, Xin Xu, Meng Peng, Shu Juan Fan, Wentao Du, Ji Xiang
Abstract	The availability of large motion capture (mocap) data has sparked a great motivation for computer animation, and the task of automatically annotating complex mocap sequences plays an important role in the efficient motion analysis. To this end, this study presents an efficient human mocap data annotation approach by using multi-view spatiotemporal feature fusion. First, the authors exploit an improved hierarchical aligned cluster analysis algorithm to divide the unknown human mocap sequence into several sub-motion clips, and each sub-motion clip incorporates a particular semantic meaning. Then, the two kinds of multi-view features, namely most informative central distances and most informative geometric angles, are discriminatively extracted and temporally modelled by a Fourier temporal pyramid to complementarily characterise each motion clip. Finally, the authors utilise the discriminant correlation analysis to fuse these two types of motion features and further employ an extreme learning machine to annotate each sub-motion clip. The extensive experiments tested on the public available database have demonstrated the effectiveness of the proposed approach in comparison with the existing counterparts.
Starting Page	269
Ending Page	276
Page Count	8
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0542
Journal	IET Signal Processing
Publisher Date	2017-10-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Animation Computer Vision And Image Processing Technique Discriminant Correlation Analysis Fourier Temporal Pyramid Human Mocap Data Annotation Approach Human Motion Capture Data Annotation Image Fusion Image Motion Analysis Informative Geometric Angles Knowledge Engineering Technique Learning in AI Learning Machine Motion Clip Multiview Spatiotemporal Feature Fusion Optical, Image And Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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