3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction

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Title: 3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction
Authors: Castro-Vargas, John Alejandro | Zapata-Impata, Brayan S. | Gil, Pablo | Garcia-Rodriguez, Jose | Torres, Fernando
Research Group/s: Automática, Robótica y Visión Artificial | Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Gesture Recognition from Video | 3D Convolutional Neural Network | Interaction human-robot
Knowledge Area: Ingeniería de Sistemas y Automática | Arquitectura y Tecnología de Computadores
Issue Date: 19-Feb-2019
Publisher: SciTePress
Citation: Castro-Vargas, J.; Zapata-Impata, B.; Gil, P.; Garcia-Rodriguez, J. and Torres, F. (2019). 3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 802-806. DOI: 10.5220/0007570208020806
Abstract: In the past, methods for hand sign recognition have been successfully tested in Human Robot Interaction (HRI) using traditional methodologies based on static image features and machine learning. However, the recognition of gestures in video sequences is a problem still open, because current detection methods achieve low scores when the background is undefined or in unstructured scenarios. Deep learning techniques are being applied to approach a solution for this problem in recent years. In this paper, we present a study in which we analyse the performance of a 3DCNN architecture for hand gesture recognition in an unstructured scenario. The system yields a score of 73% in both accuracy and F1. The aim of the work is the implementation of a system for commanding robots with gestures recorded by video in real scenarios.
Sponsor: This work was funded by the Ministry of Economy, Industry and Competitiveness from the Spanish Government through the DPI2015-68087-R and the pre-doctoral grant BES-2016-078290, by the European Commission and FEDER funds through the project COMMANDIA (SOE2/P1/F0638), action supported by Interreg-V Sudoe.
URI: http://hdl.handle.net/10045/90590
ISBN: 978-989-758-351-3
DOI: 10.5220/0007570208020806
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © 2019 by SCITEPRESS – Science and Technology Publications, Lda.
Peer Review: si
Publisher version: https://doi.org/10.5220/0007570208020806
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