Research Article
Real-Time Gesture Recognition Based On Motion Quality Analysis
@INPROCEEDINGS{10.4108/icst.intetain.2015.259608, author={C\^{e}line Jost and Igor Stankovic and Pierre De Loor and Alexis N\^{e}d\^{e}lec and Elisabetta Bevacqua}, title={Real-Time Gesture Recognition Based On Motion Quality Analysis}, proceedings={7th International Conference on Intelligent Technologies for Interactive Entertainment}, publisher={EAI}, proceedings_a={INTETAIN}, year={2015}, month={8}, keywords={gesture recognition quality motion features morphology independence}, doi={10.4108/icst.intetain.2015.259608} }- Céline Jost
Igor Stankovic
Pierre De Loor
Alexis Nédélec
Elisabetta Bevacqua
Year: 2015
Real-Time Gesture Recognition Based On Motion Quality Analysis
INTETAIN
ICST
DOI: 10.4108/icst.intetain.2015.259608
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
