loading
Documents

Research.Publish.Connect.

Paper

Authors: J. Castro-Vargas 1 ; B. Zapata-Impata 1 ; P. Gil 2 ; J. Garcia-Rodriguez 3 and F. Torres 2

Affiliations: 1 Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Alicante, Spain ; 2 Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Alicante, Spain, Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante, Spain ; 3 Dept. of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante, Spain, Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante, Spain

ISBN: 978-989-758-351-3

Keyword(s): Gesture Recognition from Video, 3D Convolutional Neural Network.

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.237.76.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@conference{icpram19,
author={J. A. Castro{-}Vargas. and B. S. Zapata{-}Impata. and P. Gil. and J. Garcia{-}Rodriguez. and F. Torres.},
title={3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={802-806},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007570208020806},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - 3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction
SN - 978-989-758-351-3
AU - Castro-Vargas, J.
AU - Zapata-Impata, B.
AU - Gil, P.
AU - Garcia-Rodriguez, J.
AU - Torres, F.
PY - 2019
SP - 802
EP - 806
DO - 10.5220/0007570208020806

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.