3D Hand Gesture Recognition by Analysing Set-of-Joints Trajectories
| Autor: | Jean-Philippe Vandeborre, Quentin De Smedt, Hazem Wannous |
|---|---|
| Přispěvatelé: | Modeling and Analysis of Static and Dynamic Shapes (3D-SAM), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut TELECOM/TELECOM Lille1, Institut Mines-Télécom [Paris] (IMT), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Euclidean space
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Riemaniann Manifold [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 02 engineering and technology Riemannian manifold Gesture recognition Hand skeleton Depth image 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Classifier (UML) Gesture |
| Zdroj: | International Conference on Pattern Recognition (ICPR) / UHA3DS 2016 workshop International Conference on Pattern Recognition (ICPR) / UHA3DS 2016 workshop, Dec 2016, Cancun, Mexico Understanding Human Activities Through 3D Sensors ISBN: 9783319918624 UHA3DS@ICPR |
| Popis: | International audience; Hand gesture recognition is recently becoming one of the most attractive field of research in Pattern Recognition. In this paper, a skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we consider the sequential data of hand geometric configuration to capture the hand shape variation, and explore the temporal character of hand motion. 3D Hand gesture are represented as a set of relevant spatiotemporal motion trajectories of hand-parts in an Euclidean space. Trajectories are then interpreted as elements lying on Riemannian manifold of shape space to capture their shape variations and achieve gesture recognition using a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two di↵erent numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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