Name
Abstract | ||
|---|---|---|
This paper presents a robust and anticipative realtime gesture recognition and its motion quality analysis module. By utilizing a motion capture device, the system recognizes gestures performed by a human, where the recognition process is based on skeleton analysis and motion features computation. Gestures are collected from a single person. Skeleton joints are used to compute features which are stored in a reference database, and Principal Component Analysis (PCA) is computed to select the most important features, useful in discriminating gestures. During real-time recognition, using distance measures, real-time selected features are compared to the reference database to find the most similar gesture. Our evaluation results show that: i) recognition delay is similar to human recognition delay, ii) our module can recognize several gestures performed by different people and is morphology-independent, and iii) recognition rate is high: all gestures are recognized during gesture stroke. Results also show performance limits. |
Year | Venue | Field |
|---|---|---|
2015 | ICST Trans. e-Education e-Learning | Computer science,Gesture recognition,Human–computer interaction,Multimedia |
DocType | Volume | Issue |
Journal | 2 | 8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Céline Jost | 1 | 10 | 4.05 |
| Igor Stankovic | 2 | 5 | 1.51 |
| Pierre De Loor | 3 | 77 | 17.09 |
| Alexis Nédélec | 4 | 23 | 4.29 |
| Elisabetta Bevacqua | 5 | 337 | 28.51 |