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.
Lingua originale | English |
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Pagine | 216-224 |
Numero di pagine | 9 |
Stato di pubblicazione | Published - 2017 |
All Science Journal Classification (ASJC) codes
- ???subjectarea.asjc.2200.2207???
- ???subjectarea.asjc.1700.1700???