Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-61566-0_21 | COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS, CISIS-2017 |
Keywords | Field | DocType |
Gesture recognition,Real-time systems,Constrained optimization | Training set,Gesture,Computer science,Gesture recognition,Speech recognition,Artificial intelligence,Robotics,Constrained optimization | Conference |
Volume | ISSN | Citations |
611 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabrizio Milazzo | 1 | 51 | 6.25 |
| Vito Gentile | 2 | 30 | 9.90 |
| Antonio Gentile | 3 | 72 | 9.40 |
| Salvatore Sorce | 4 | 130 | 20.48 |