Indexing of Motion Capture Data Using Feature Vectors Derived from Posture Variation (Preprint)

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概要

• 論文の詳細を見る

• Recently several large-scale databases of motion-capture data streams have been constructed. We present a novel method to index motion-capture data streams in such databases. We pay attention to posture variation; the impression of the visual aspect of the whole body is regarded as important. The spatial distribution of body segments is statistically summarized as a feature vector having only 12 dimensions. The experimental results showed that the feature vector we introduced provided properties comparable to those of the methods previously proposed, even though its dimensionality is extremely low.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.358------------------------------

• 2013-04-15

著者

• Takeshi Miura

  Graduate School of Engineering and Resource Science, Akita University

• Hideo Tamamoto

  Graduate School of Engineering and Resource Science, Akita University

• Takaaki Kaiga

  Graduate School Of Engineering And Resource Science Akita University | Digital Art Factory Warabi-za

• Takaaki Kaiga

  Graduate School of Engineering and Resource Science, Akita University|Digital Art Factory, Warabi-za Co., Ltd.

• Naho Matsumoto

  Faculty of Education and Human Studies, Akita University

• Hiroaki Katsura

  Faculty of Education and Human Studies, Akita University

• Katsubumi Tajima

  Graduate School of Engineering and Resource Science, Akita University

関連論文

• Adaptation of Grouping Structure Analysis in GTTM to Hierarchical Segmentation of Dance Motion
• Extraction of Motion Characteristics in Dances by Statistical Analysis of Joint Motions
• Indexing of Motion Capture Data Using Feature Vectors Derived from Posture Variation (Preprint)
• Adaptive Keypose Extraction from Motion Capture Data (Preprint)

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