Real-time Hand Pose Tracking and Classification for Natural Human-Robot Control

Bruno Lima, Givanildo N. Júnior, Lucas Amaral, Thales Vieira, Bruno Ferreira, Tiago Vieira

Abstract

We present a human-robot natural interaction approach based on teleoperation through body gestures. More specifically, we propose an interface where the user can use his hand to intuitively control the position and status (open/closed) of a robotic arm gripper. In this work, we employ a 6-DOF (six degrees-of-freedom) industrial manipulator which mimics user movements in real-time, positioning the end effector as if the individual was looking into a mirror, entailing a natural and intuitive interface. The controlling hand of the user is tracked using body skeletons acquired from a Microsoft Kinect sensor, while a Convolutional Neural Network recognizes whether the hand is opened or closed using depth data. The network was trained on hand images collected from several individuals, in different orientations, resulting in a robust classifier that performs well regardless of user location or orientation. There is no need for wearable devices, such as gloves or wristbands. We present results of experiments that reveal high performance of the proposed approach to recognize both the user hand position and its status (open/closed); and experiments to demonstrate the robustness and applicability of the proposed approach to industrial tasks.

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Paper Citation


in Harvard Style

Lima B., N. Júnior G., Amaral L., Vieira T., Ferreira B. and Vieira T. (2019). Real-time Hand Pose Tracking and Classification for Natural Human-Robot Control.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 832-839. DOI: 10.5220/0007384608320839


in Bibtex Style

@conference{visapp19,
author={Bruno Lima and Givanildo N. Júnior and Lucas Amaral and Thales Vieira and Bruno Ferreira and Tiago Vieira},
title={Real-time Hand Pose Tracking and Classification for Natural Human-Robot Control},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={832-839},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007384608320839},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Real-time Hand Pose Tracking and Classification for Natural Human-Robot Control
SN - 978-989-758-354-4
AU - Lima B.
AU - N. Júnior G.
AU - Amaral L.
AU - Vieira T.
AU - Ferreira B.
AU - Vieira T.
PY - 2019
SP - 832
EP - 839
DO - 10.5220/0007384608320839