日常動作の概念関係と隠れマルコフモデルを利用した動作のオンライン分節化 Online Segmentation of Actions Using Hidden Markov Models and Conceptional Relations of Daily Actions

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著者

    • 森 武俊 MORI Taketoshi
    • 東京大学大学院情報学環・学際情報学府 Graduate School of Interdisciplinary Information Studies of the University of Tokyo
    • 祢次金 佑 NEJIGANE Yu
    • 東京大学大学院情報理工学研究科 Graduate School of Information Science and Technology of the University of Tokyo
    • 佐藤 知正 SATO Tomomasa
    • 東京大学大学院情報理工学研究科 Graduate School of Information Science and Technology of the University of Tokyo

抄録

In this paper, we propose a robust online action recognition algorithm with a segmentation scheme that detects start and end points of action occurrences. Specifically, the alogorithm estimates reliably what kind of actions occurring at present time. The algorithm has following characteristics. (1) The algorithm incorporates human knowledge about relations between action names in order to toughen the recognition, thus it labels robustly multiple action names at the same time. (2) The algorithm uses time-series Action Probability that represents the likelihood of each action occurrence at every frame time. The Action Probability is obtained from time-series human motion using support vector machine. (3) The algorithm can detect robustly and immediately the segmental points using classification technique with hidden Markov models (HMIs) . The experimental results using real motion capture data show that our algorithm not only prevents the system from making unnecessary segments due to the error of time-series Action Probability but also decreases effectively the latency for detecting the segmental points.

収録刊行物

  • 日本ロボット学会誌 = Journal of Robotics Society of Japan  

    日本ロボット学会誌 = Journal of Robotics Society of Japan 25(1), 130-137, 2007-01-15 

    The Robotics Society of Japan

参考文献:  26件

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被引用文献:  4件

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各種コード

  • NII論文ID(NAID)
    10018695638
  • NII書誌ID(NCID)
    AN00141189
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    02891824
  • NDL 記事登録ID
    8635929
  • NDL 雑誌分類
    ZN11(科学技術--機械工学・工業)
  • NDL 請求記号
    Z16-1325
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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