Real-Time Classification of Gestures Using Kinect, with Emphasis on Dance (US)

Gamefest 2011 Presentation
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      We developed a system to quickly recognize dance gestures by using Kinect skeletal motion. Key components include a low-dimensional angular representation of the skeleton, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping. We train the classifier on a benchmark comprising 28 gesture classes and hundreds of recorded gesture instances, and obtain an average accuracy of 95 percent for approximately 4-second skeletal motion recordings. Most gestures can be distinguished in 1 to 2 seconds.
  • Supported Operating System

    Windows 7

      PowerPoint, WMA
    • PowerPoint presentation and WMA
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