Heterogeneous hand gesture recognition using 3D dynamic skeletal data
| Autor: | Quentin De Smedt, Jean-Philippe Vandeborre, Hazem Wannous |
|---|---|
| Přispěvatelé: | Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT), 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Feature vector Fisher kernel [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering Pattern recognition 02 engineering and technology Kinematics Binary classification Gesture recognition Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Pose Classifier (UML) Software Gesture |
| Zdroj: | Computer Vision and Image Understanding Computer Vision and Image Understanding, Elsevier, 2019, 181, pp.60-72. ⟨10.1016/j.cviu.2019.01.008⟩ Computer Vision and Image Understanding, 2019, 181, pp.60-72. ⟨10.1016/j.cviu.2019.01.008⟩ |
| ISSN: | 1077-3142 1090-235X |
| DOI: | 10.1016/j.cviu.2019.01.008⟩ |
| Popis: | International audience; Hand gestures are the most natural and intuitive non-verbal communication medium while interacting with a computer, and related research efforts have recently boosted interest. Additionally, the identifiable features of the hand pose provided by current commercial inexpensive depth cameras can be exploited in various gesture recognition based systems, especially for Human-Computer Interaction. In this paper, we focus our attention on 3D dynamic gesture recognition systems using the hand pose information. Specifically, we use the natural structure of the hand topology-called later hand skeletal data-to extract effective hand kinematic descriptors from the gesture sequence. Descriptors are then encoded in a statistical and temporal representation using respectively a Fisher kernel and a multi-level temporal pyramid. A linear SVM classifier can be applied directly on the feature vector computed over the whole presegmented gesture to perform the recognition. Furthermore, for early recognition from continuous stream, we introduced a prior gesture detection phase achieved using a binary classifier before the final gesture recognition. The proposed approach is evaluated on three hand gesture datasets containing respectively 10, 14 and 25 gestures with specific challenging tasks. Also, we conduct an experiment to assess the influence of depth-based hand pose estimation on our approach. Experimental results demonstrate the potential of the proposed solution in terms of hand gesture recognition and also for a low-latency gesture recognition. Comparative results with state-of-the-art methods are reported. |
| Databáze: | OpenAIRE |
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