The online gait measurement for characteristic gait animation synthesis

Yasushi Makihara, Mayu Okumura, Yasushi Yagi, Shigeo Morishima

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper presents a method to measure online the gait features from the gait silhouette images and to synthesize characteristic gait animation for an audience-participant digital entertainment. First, both static and dynamic gait features are extracted from the silhouette images captured by an online gait measurement system. Then, key motion data for various gaits are captured and a new motion data is synthesized by blending key motion data. Finally, blend ratios of the key motion data are estimated to minimize gait feature errors between the blended model and the online measurement. In experiments, the effectiveness of gait feature extraction were confirmed by using 100 subjects from OU-ISIR Gait Database and characteristic gait animations were created based on the measured gait features.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages325-334
    Number of pages10
    Volume6773 LNCS
    EditionPART 1
    DOIs
    Publication statusPublished - 2011
    Event4th International Conference on Virtual and Mixed Reality, Held as Part of HCI International 2011 - Orlando, FL
    Duration: 2011 Jul 92011 Jul 14

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 1
    Volume6773 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other4th International Conference on Virtual and Mixed Reality, Held as Part of HCI International 2011
    CityOrlando, FL
    Period11/7/911/7/14

    Fingerprint

    Gait
    Animation
    Synthesis
    Feature extraction
    Silhouette
    Motion
    Experiments
    On-line Measurement
    Measurement System
    Feature Extraction
    Minimise

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Makihara, Y., Okumura, M., Yagi, Y., & Morishima, S. (2011). The online gait measurement for characteristic gait animation synthesis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6773 LNCS, pp. 325-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6773 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-22021-0_36

    The online gait measurement for characteristic gait animation synthesis. / Makihara, Yasushi; Okumura, Mayu; Yagi, Yasushi; Morishima, Shigeo.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6773 LNCS PART 1. ed. 2011. p. 325-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6773 LNCS, No. PART 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Makihara, Y, Okumura, M, Yagi, Y & Morishima, S 2011, The online gait measurement for characteristic gait animation synthesis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 6773 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6773 LNCS, pp. 325-334, 4th International Conference on Virtual and Mixed Reality, Held as Part of HCI International 2011, Orlando, FL, 11/7/9. https://doi.org/10.1007/978-3-642-22021-0_36
    Makihara Y, Okumura M, Yagi Y, Morishima S. The online gait measurement for characteristic gait animation synthesis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 6773 LNCS. 2011. p. 325-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-22021-0_36
    Makihara, Yasushi ; Okumura, Mayu ; Yagi, Yasushi ; Morishima, Shigeo. / The online gait measurement for characteristic gait animation synthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6773 LNCS PART 1. ed. 2011. pp. 325-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
    @inproceedings{70dc531b11474b09b0938c37e49e6cb2,
    title = "The online gait measurement for characteristic gait animation synthesis",
    abstract = "This paper presents a method to measure online the gait features from the gait silhouette images and to synthesize characteristic gait animation for an audience-participant digital entertainment. First, both static and dynamic gait features are extracted from the silhouette images captured by an online gait measurement system. Then, key motion data for various gaits are captured and a new motion data is synthesized by blending key motion data. Finally, blend ratios of the key motion data are estimated to minimize gait feature errors between the blended model and the online measurement. In experiments, the effectiveness of gait feature extraction were confirmed by using 100 subjects from OU-ISIR Gait Database and characteristic gait animations were created based on the measured gait features.",
    author = "Yasushi Makihara and Mayu Okumura and Yasushi Yagi and Shigeo Morishima",
    year = "2011",
    doi = "10.1007/978-3-642-22021-0_36",
    language = "English",
    isbn = "9783642220203",
    volume = "6773 LNCS",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    number = "PART 1",
    pages = "325--334",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    edition = "PART 1",

    }

    TY - GEN

    T1 - The online gait measurement for characteristic gait animation synthesis

    AU - Makihara, Yasushi

    AU - Okumura, Mayu

    AU - Yagi, Yasushi

    AU - Morishima, Shigeo

    PY - 2011

    Y1 - 2011

    N2 - This paper presents a method to measure online the gait features from the gait silhouette images and to synthesize characteristic gait animation for an audience-participant digital entertainment. First, both static and dynamic gait features are extracted from the silhouette images captured by an online gait measurement system. Then, key motion data for various gaits are captured and a new motion data is synthesized by blending key motion data. Finally, blend ratios of the key motion data are estimated to minimize gait feature errors between the blended model and the online measurement. In experiments, the effectiveness of gait feature extraction were confirmed by using 100 subjects from OU-ISIR Gait Database and characteristic gait animations were created based on the measured gait features.

    AB - This paper presents a method to measure online the gait features from the gait silhouette images and to synthesize characteristic gait animation for an audience-participant digital entertainment. First, both static and dynamic gait features are extracted from the silhouette images captured by an online gait measurement system. Then, key motion data for various gaits are captured and a new motion data is synthesized by blending key motion data. Finally, blend ratios of the key motion data are estimated to minimize gait feature errors between the blended model and the online measurement. In experiments, the effectiveness of gait feature extraction were confirmed by using 100 subjects from OU-ISIR Gait Database and characteristic gait animations were created based on the measured gait features.

    UR - http://www.scopus.com/inward/record.url?scp=79960434973&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=79960434973&partnerID=8YFLogxK

    U2 - 10.1007/978-3-642-22021-0_36

    DO - 10.1007/978-3-642-22021-0_36

    M3 - Conference contribution

    SN - 9783642220203

    VL - 6773 LNCS

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 325

    EP - 334

    BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    ER -