Adaptive Keypose Extraction from Motion Capture Data

• Item type: Journal(1)
• 公開日: 2013-12-15
• タイトル: Adaptive Keypose Extraction from Motion Capture Data
• タイトル / 言語: en
• タイトル: Adaptive Keypose Extraction from Motion Capture Data
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] motion capture, keypose extraction, motion characteristic
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Engineering and Resource Science, Akita University
• 著者所属: Graduate School of Engineering and Resource Science, Akita University／Digital Art Factory, Warabi-za Co., Ltd.
• 著者所属: Faculty of Education and Human Studies, Akita University
• 著者所属: Graduate School of Engineering and Resource Science, Akita University
• 著者所属: Venture Business Laboratory, Akita University
• 著者所属: Akita University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering and Resource Science, Akita University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering and Resource Science, Akita University / Digital Art Factory, Warabi-za Co., Ltd.
• 著者所属(英): en
• 著者所属(英): Faculty of Education and Human Studies, Akita University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering and Resource Science, Akita University
• 著者所属(英): en
• 著者所属(英): Venture Business Laboratory, Akita University
• 著者所属(英): en
• 著者所属(英): Akita University
• 著者名: Takeshi, Miura; Takaaki, Kaiga; Hiroaki, Katsura; Katsubumi, Tajima; Takeshi, Shibata; Hideo, Tamamoto
• 著者名(英): Takeshi, Miura; Takaaki, Kaiga; Hiroaki, Katsura; Katsubumi, Tajima; Takeshi, Shibata; Hideo, Tamamoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper, we present a novel method to extract keyposes from motion-capture data streams. It adaptively extracts keyposes in response to the motion characteristics of a given data stream. We adopt an approach to detect local minima in the temporal variation of motion speed. In the developed algorithm, the intensity of each local minimum is first evaluated by using a set of signals; it is obtained by applying a set of low-pass filters to a one-dimensional motion-speed data stream. The cut-off frequencies of the filters are distributed over a wide frequency range. By adding up the speed-descent values of each local minimum over all the signals, we exhaustively obtain the information on its intensity provided at all the time-scale levels covered by a given data stream. Then, the obtained intensity values are categorized by a clustering algorithm; the local minima categorized as those of little significance are deleted and the remaining ones are fixed as those giving keyposes. Experimental results showed that the present method provided results comparable to the best of those given by the methods previously proposed. This was achieved without readjusting the values of parameters used in the algorithm. Readjustment was indispensable for the other methods to obtain good results.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.22(2014) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.22.67; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper, we present a novel method to extract keyposes from motion-capture data streams. It adaptively extracts keyposes in response to the motion characteristics of a given data stream. We adopt an approach to detect local minima in the temporal variation of motion speed. In the developed algorithm, the intensity of each local minimum is first evaluated by using a set of signals; it is obtained by applying a set of low-pass filters to a one-dimensional motion-speed data stream. The cut-off frequencies of the filters are distributed over a wide frequency range. By adding up the speed-descent values of each local minimum over all the signals, we exhaustively obtain the information on its intensity provided at all the time-scale levels covered by a given data stream. Then, the obtained intensity values are categorized by a clustering algorithm; the local minima categorized as those of little significance are deleted and the remaining ones are fixed as those giving keyposes. Experimental results showed that the present method provided results comparable to the best of those given by the methods previously proposed. This was achieved without readjusting the values of parameters used in the algorithm. Readjustment was indispensable for the other methods to obtain good results.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.22(2014) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.22.67; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 54, 号 12, 発行日 2013-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764