research-article

Real-time classification of dance gestures from skeleton animation

Authors:

Michalis Raptis,

Darko Kirovski,

Hugues HoppeAuthors Info & Claims

SCA '11: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation

Pages 147 - 156

https://doi.org/10.1145/2019406.2019426

Published: 05 August 2011 Publication History

Abstract

We present a real-time gesture classification system for skeletal wireframe motion. Its key components include an angular representation of the skeleton designed for recognition robustness under noisy input, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping to evaluate the difference in motion between an acquired gesture and an oracle for the matching gesture. While the first and last tools are generic in nature and could be applied to any gesture-matching scenario, the classifier is conceived based on the assumption that the input motion adheres to a known, canonical time-base: a musical beat. On a benchmark comprising 28 gesture classes, hundreds of gesture instances recorded using the XBOX Kinect platform and performed by dozens of subjects for each gesture class, our classifier has an average accuracy of 96:9%, for approximately 4-second skeletal motion recordings. This accuracy is remarkable given the input noise from the real-time depth sensor.

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Wang ZYan JYan GYu B(2025)Multi-scale control and action recognition based human-robot collaboration framework facing new generation intelligent manufacturingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10284791(102847)Online publication date: Feb-2025
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Laattala MPiitulainen RAdy NTamariz MHämäläinen P(2024)WAVE: Anticipatory Movement Visualization for VR DancingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642145(1-9)Online publication date: 11-May-2024
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Sucharitakul SKetaiam KPhuengngern CHongyeesibed S(2024)Innovative toolkit for objective evaluation of traditional Thai dancing by intern teachers using Kinect, MediaPipe, and microcontroller technologyCogent Arts & Humanities10.1080/23311983.2024.243331311:1Online publication date: 25-Nov-2024
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Index Terms

Real-time classification of dance gestures from skeleton animation
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Published In

cover image ACM Conferences

SCA '11: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation

August 2011

297 pages

ISBN:9781450309233

DOI:10.1145/2019406

Conference Chairs:
Karen Liu
Georgia Institute of Technology
,
Robert Bridson
University of British Columbia
,
Editor:
Stephen N. Spencer
University of Washington
,
Program Chairs:
Adam Bargteil
University of Utah
,
Michiel van de Panne
University of British Columbia

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
EUROGRAPHICS: The European Association for Computer Graphics

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Association for Computing Machinery

New York, NY, United States

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Published: 05 August 2011

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SCA '11: The ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2011

August 5 - 7, 2011

British Columbia, Vancouver, Canada

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Overall Acceptance Rate 183 of 487 submissions, 38%

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https://doi.org/10.1016/j.rcim.2024.102847
Laattala MPiitulainen RAdy NTamariz MHämäläinen P(2024)WAVE: Anticipatory Movement Visualization for VR DancingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642145(1-9)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642145
Sucharitakul SKetaiam KPhuengngern CHongyeesibed S(2024)Innovative toolkit for objective evaluation of traditional Thai dancing by intern teachers using Kinect, MediaPipe, and microcontroller technologyCogent Arts & Humanities10.1080/23311983.2024.243331311:1Online publication date: 25-Nov-2024
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