Vision-based gesture recognition system for human-computer interaction

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

trigueiros, p (Autor) Outra; ribeiro, f (Autor) Outra; reis, lp (Autor) FCUP

Ata de Conferência Internacional

Título: Computational Vision and Medical Image Processing IV

Páginas: 137-142

Proceedings of the IV ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing: VipIMAGE 2013 (Funchal, Portugal, 14-16 October 2013)

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 5 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-009-NDJ

DOI: 10.1201/b15810-27

Abstract (EN): Hand gesture recognition, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. This work intends to study and implement a solution, generic enough, able to interpret user commands, composed of a set of dynamic and static gestures, and use those solutions to build an application able to work in a real-time human-computer interaction systems. The proposed solution is composed of two modules controlled by a FSM (Finite State Machine): a real time hand tracking and feature extraction system, supported by a SVM (Support Vector Machine) model for static hand posture classification and a set of HMMs (Hidden Markov Models) for dynamic single stroke hand gesture recognition. The experimental results showed that the system works very reliably, being able to recognize the set of defined commands in real-time. The SVM model for hand posture classification, trained with the selected hand features, achieved an accuracy of 99,2%. The proposed solution as the advantage of being computationally simple to train and use, and at the same time generic enough, allowing its application in any robot/system command interface.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6