Towards Automatic Gesture Stroke Detection

  • Gebre B
  • Wittenburg P
  • Lenkiewicz P
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Abstract

Automatic annotation of gesture strokes is important for many gesture and sign language researchers. The unpredictable diversity of human gestures and video recording conditions require that we adopt a more adaptive case-by-case annotation model. In this paper, we present a work-in progress annotation model that allows a user to a) track hands/face b) extract features c) distinguish strokes from non-strokes. The hands/face tracking is done with color matching algorithms and is initialized by the user. The initialization process is supported with immediate visual feedback. Sliders are also provided to support a user-friendly adjustment of skin color ranges. After successful initialization, features related to positions, orientations and speeds of tracked hands/face are extracted using unique identifiable features (corners) from a window of frames and are used for training a learning algorithm. Our prelim- inary results for stroke detection under non-ideal video conditions are promising and showthe potential applicability of our methodology.

Author-supplied keywords

  • annotation
  • gesture
  • stroke

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Authors

  • Binyam Gebrekidan Gebre

  • Peter Wittenburg

  • Przemyslaw Lenkiewicz

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