Abstract
This work classifies top-view hand-gestures observed by a Time of Flight (ToF) camera using Long Short-Term Memory (LSTM) architecture of neural networks. We demonstrate a performance improvement by a two-phase classification. Therefore we reduce the number of classes to be separated in each phase and combine the output probabilities. The modified system architecture achieves an average cross-validation accuracy of 90.75% on a 9-gesture dataset. This is demonstrated to be an improvement over the single all-class LSTM approach. The networks are trained to predict the class-label continuously during the sequence. A frame-based gesture prediction, using accumulated gesture probabilities per frame of the video sequence, is introduced. This eliminates the latency due to prediction of gesture at the end of the sequence as is usually the case with majority voting based methods.
This work is supported by the National Research Fund, Luxembourg, under the AFR project 7019190.
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Tewari, A., Taetz, B., Grandidier, F., Stricker, D. (2016). Two Phase Classification for Early Hand Gesture Recognition in 3D Top View Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_33
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DOI: https://doi.org/10.1007/978-3-319-50835-1_33
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