Real-Time Gesture Recognition Based On Motion Quality Analysis
Journal Title: EAI Endorsed Transactions on e-Learning - Year 2015, Vol 2, Issue 8
Abstract
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
Authors and Affiliations
Céline Jost, Igor Stankovic, Pierre De Loor, Alexis Nédélec, Elisabetta Bevacqua
Keywords
Related Articles
- EP ID EP45957
- DOI http://dx.doi.org/10.4108/icst.intetain.2015.259608
- Views 302
- Downloads 0
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