Abstract
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.
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Notes
- 1.
A joint is defined as the point of conjunction between two adjacent bones of the human skeleton.
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Milazzo, F., Gentile, V., Gentile, A., Sorce, S. (2018). Real-Time Body Gestures Recognition Using Training Set Constrained Reduction. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_21
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DOI: https://doi.org/10.1007/978-3-319-61566-0_21
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