DING Wenwen, LIU Kai, XU Biao, CHENG Fei. Skeleton-Based Human Action Recognition via Screw Matrices[J]. Chinese Journal of Electronics, 2017, 26(4): 790-796. doi: 10.1049/cje.2017.06.012
Citation: DING Wenwen, LIU Kai, XU Biao, CHENG Fei. Skeleton-Based Human Action Recognition via Screw Matrices[J]. Chinese Journal of Electronics, 2017, 26(4): 790-796. doi: 10.1049/cje.2017.06.012

Skeleton-Based Human Action Recognition via Screw Matrices

doi: 10.1049/cje.2017.06.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61571345), the Fundamental Research Funds for the Central Universities (No.K5051203005), the National Natural Science Foundation of China (No.6150110247), and the Natural Science Foundation of the Anhui Higher Education Institutions of China (No.KJ2016A625, No.KJ2017A376, No.KJ2017A377).
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  • Corresponding author: LIU Kai (corresponding author) received the B.S. and M.S. degrees in computer science and the Ph.D. degree in signal processing from Xidian University, Xian, China, in 1999, 2002, and 2005, respectively. Currently, he is a professor of Computer science and technology with the Xidian University. His major research interests include VLSI architecture design and image coding. (Tel:+86 13892810532, Email: kailiu@mail.xidian.edu.cn)
  • Received Date: 2016-04-08
  • Rev Recd Date: 2016-11-02
  • Publish Date: 2017-07-10
  • With the recent advent of low-cost acquisition depth cameras, extracting 3D body skeleton has become relatively easier, which significantly lighten many difficulties in 2D videos including occlusions, shadows and background extraction, etc. Directly perceived features, for example points, lines and planes, can be easily extracted from 3D videos such that we can employ rigid motions to represent skeletal motions in a geometric way. We apply screw matrices, acquired by using rotations and translations in 3D space, to model single and multi-body rigid motion. Since screw matrices are members of the special Euclidean group SE(3), an action can be represented as a point on a Lie group, which is a differentiable manifold. Using Lie-algebraic properties of screw algebra, isomorphic to se(3), the classical algorithms of machine learning in vector space can be expanded to manifold space. We evaluate our approached on three public 3D action datasets: MSR Action3D dataset, UCF Kinect dataset and Florence3D-Action Dataset. The experimental results show that our approaches either match or exceed state-of-the-art skeleton-based human action recognition approaches.
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