Real-Time Gesture Recognition Based On Motion Quality Analysis
This paper presents a robust and anticipative real-time 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....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Book |
| Published: |
European Alliance for Innovation (EAI),
2015-08-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper presents a robust and anticipative real-time 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 |
|---|---|
| Item Description: | 10.4108/icst.intetain.2015.259608 2032-9253 |