Real-Time Gesture Recognition Based On Motion Quality Analysis

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.
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Contributor : Céline Jost <>
Submitted on : Monday, June 22, 2015 - 3:06:31 PM
Last modification on : Thursday, October 17, 2019 - 12:36:40 PM
Long-term archiving on : Tuesday, September 15, 2015 - 8:32:46 PM


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  • HAL Id : hal-01166273, version 1


Céline Jost, Igor Stankovic, Pierre de Loor, Alexis Nédélec, Elisabetta Bevacqua. Real-Time Gesture Recognition Based On Motion Quality Analysis. 7th International Conference on Intelligent Technologies for Interactive Entertainment - INTETAIN 2015, Jun 2015, Torino, Italy. ⟨hal-01166273⟩



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