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

J. Castro-Vargas, B. Zapata-Impata, P. Gil, J. Garcia-Rodriguez, F. Torres

2019

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@conference{icpram19,
author={J. Castro-Vargas and B. 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},
}

in EndNote Style

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