Real-Time Classification of Dance Gesturesfrom Skeleton Animation

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dc.contributor.authorRaptis, Michalisen_US
dc.contributor.authorKirovski, Darkoen_US
dc.contributor.authorHoppe, Huguesen_US
dc.contributor.editorA. Bargteil and M. van de Panneen_US
dc.date.accessioned2013-10-31T10:28:46Z
dc.date.available2013-10-31T10:28:46Z
dc.date.issued2011en_US
dc.description.abstractWe 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 timewarping 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.en_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animationen_US
dc.identifier.isbn978-1-4503-0923-3en_US
dc.identifier.issn1727-5288en_US
dc.identifier.urihttps://doi.org/10.2312/SCA/SCA11/147-156en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-DimensionalGraphics and Realism-Animationen_US
dc.titleReal-Time Classification of Dance Gesturesfrom Skeleton Animationen_US
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SCA 11: Eurographics/SIGGRAPH Symposium on Computer Animation