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Descripción
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| New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system. | |
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Internacional
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Si |
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Nombre congreso
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SPIE Int. Conf. Unmanned Systems Technology XVI |
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Tipo de participación
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960 |
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Lugar del congreso
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Baltimore (MA), USA |
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Revisores
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Si |
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ISBN o ISSN
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0277-786X |
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DOI
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10.1117/12.2053244 |
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Fecha inicio congreso
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05/05/2014 |
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Fecha fin congreso
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09/05/2014 |
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Desde la página
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1 |
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Hasta la página
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11 |
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Título de las actas
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SPIE Int. Conf. Unmanned Systems Technology XVI |