Time-Series Human Motion Analysis with Kernels Derived from Learned Switching Linear Dynamics

• Mori Taketoshi

  The University of Tokyo

• Shimosaka Masamichi

  The University of Tokyo

• Harada Tatsuya

  The University of Tokyo

• Sato Tomomasa

  The University of Tokyo

抄録

In this paper, we propose a novel kernel computation algorithm between time-series human motion data for online action recognition. The proposed kernel is based on probabilistic models called switching linear dynamics (SLDs). SLD is one of the powerful tools for tracking, analyzing and classifying human complex time-series motion. The proposed kernel incorporates information about the latent variables in SLDs. The empirical evaluation using real motion data shows that a classifier using SVM with our proposed kernel has much better performance than the classifiers with some conventional kernel techniques. Another experimental result using kernel principal component analysis shows that the proposed kernel has excellent performance in extracting and separating different action categories, such as walking and running.

収録刊行物

• Information and Media Technologies

  Information and Media Technologies 1 (1), 314-325, 2006

  Information and Media Technologies 編集運営会議

キーワード

• mixed-state dynamics
• marginalized kernel
• probabilistic product kernel
• motion capture

詳細情報

• CRID: 1390001205265233664
• NII論文ID: 130000058416
• DOI: 10.11185/imt.1.314
• ISSN: 18810896
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可