Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Neto, P (Autor) Outra; Pereira, D (Autor) Outra; Pires, JN (Autor) Outra; António Paulo Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

Páginas: 178-183

IEEE International Conference on Robotics and Automation (ICRA)

Karlsruhe, GERMANY, MAY 06-10, 2013

Indexação

ISI Web of Knowledge - 34 Citações

Scopus - 51 Citações

Outras Informações

ID Authenticus: P-008-GD2

DOI: 10.1109/icra.2013.6630573

Abstract (EN): New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the non-communicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6