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Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition

  • Pei Wang
  • Chunfeng YuanEmail author
  • Weiming Hu
  • Bing Li
  • Yanning Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)

Abstract

Most of existing skeleton-based representations for action recognition can not effectively capture the spatio-temporal motion characteristics of joints and are not robust enough to noise from depth sensors and estimation errors of joints. In this paper, we propose a novel low-level representation for the motion of each joint through tracking its trajectory and segmenting it into several semantic parts called motionlets. During this process, the disturbance of noise is reduced by trajectory fitting, sampling and segmentation. Then we construct an undirected complete labeled graph to represent a video by combining these motionlets and their spatio-temporal correlations. Furthermore, a new graph kernel called subgraph-pattern graph kernel (SPGK) is proposed to measure the similarity between graphs. Finally, the SPGK is directly used as the kernel of SVM to classify videos. In order to evaluate our method, we perform a series of experiments on several public datasets and our approach achieves a comparable performance to the state-of-the-art approaches.

Keywords

3D human action recognition Graph kernel Skeleton motion 

Notes

Acknowledgments

This work is partly supported by the 973 basic research program of China (Grant No. 2014CB349303), the Natural Science Foundation of China (Grant No. 61472421, 61472420, 61303086, 61370185, 61472063), the Natural Science Foundation of Guangdong Province (Grant No. S2013010013432, S2013010015940), and the Strategic Priority Research Program of the CAS (Grant No. XDB02070003).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pei Wang
    • 1
  • Chunfeng Yuan
    • 1
    Email author
  • Weiming Hu
    • 1
  • Bing Li
    • 1
  • Yanning Zhang
    • 2
  1. 1.CAS Center for Excellence in Brain Science and Intelligence TechnologyNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina

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