Towards Automatic Gesture Stroke Detection
Binyam Gebrekidan Gebre, Peter Wittenburg, Przemyslaw Lenkiewicz · 2012
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 preliminary results for stroke detection under non-ideal video conditions are promising and show the potential applicability of our methodology.