3D Gesture Recognition by Superquadrics

Ilya Afanasyev, Mariolino De Cecco

2013

Abstract

This paper presents 3D gesture recognition and localization method based on processing 3D data of hands in color gloves acquired by 3D depth sensor, like Microsoft Kinect. RGB information of every 3D datapoints is used to segment 3D point cloud into 12 parts (a forearm, a palm and 10 for fingers). The object (a hand with fingers) should be a-priori known and anthropometrically modeled by SuperQuadrics (SQ) with certain scaling and shape parameters. The gesture (pose) is estimated hierarchically by RANSAC-object search with a least square fitting the segments of 3D point cloud to corresponding SQ-models: at first – a pose of the hand (forearm & palm), and then positions of fingers. The solution is verified by evaluating the matching score, i.e. the number of inliers corresponding to the appropriate distances from SQ surfaces and 3D datapoints, which are satisfied to an assigned distance threshold.

Paper Citation

in Harvard Style

Afanasyev I. and De Cecco M. (2013). 3D Gesture Recognition by Superquadrics . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 429-433. DOI: 10.5220/0004348404290433

in Bibtex Style

@conference{visapp13,
author={Ilya Afanasyev and Mariolino De Cecco},
title={3D Gesture Recognition by Superquadrics},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={429-433},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004348404290433},
isbn={978-989-8565-48-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - 3D Gesture Recognition by Superquadrics
SN - 978-989-8565-48-8
AU - Afanasyev I.
AU - De Cecco M.
PY - 2013
SP - 429
EP - 433
DO - 10.5220/0004348404290433