Gesture recognition is an emerging cross-discipline research field, which aims at interpreting human gestures and associating them to a well-defined meaning. It has been used as a mean for supporting human to machine interaction in several applications of robotics, artificial intelligence, and machine learning. In this paper, we propose a system able to recognize human body gestures which implements a constrained training set reduction technique. This allows the system for a real-time execution. The system has been tested on a publicly available dataset of 7,000 gestures, and experimental results have highlighted that at the cost of a little decrease in the maximum achievable recognition accuracy, the required time for recognition can be dramatically reduced.
CITATION STYLE
Milazzo, F., Gentile, V., Gentile, A., & Sorce, S. (2018). Real-time body gestures recognition using training set constrained reduction. In Advances in Intelligent Systems and Computing (Vol. 611, pp. 216–224). Springer Verlag. https://doi.org/10.1007/978-3-319-61566-0_21
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