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.
@inproceedings{gebre-etal-2012-towards:lrec,
author = {Gebre, Binyam Gebrekidan and Wittenburg, Peter and Lenkiewicz, Przemyslaw},
title = {Towards Automatic Gesture Stroke Detection},
pages = {231--235},
editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u g}an, Mehmet U{\u g}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
booktitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Istanbul, Turkey},
day = {21--27},
month = may,
year = {2012},
isbn = {978-2-9517408-7-7},
language = {english},
url = {http://www.lrec-conf.org/proceedings/lrec2012/pdf/454_Paper.pdf}
}