Gestural teleoperation of a mobile robot based on visual recognition of sign language static handshapes

被引：0

作者：

Tzafestas, C. ^{[1
]}

Mitsou, N. ^{[1
]}

Georgakarakos, N. ^{[1
]}

Diamanti, O. ^{[1
]}

Maragos, P. ^{[1
]}

Fotinea, S. -E. ^{[2
]}

Efthimiou, E. ^{[2
]}

机构：

[1] Natl Tech Univ Athens, Sch Elect & Comp Engn, Zographou Campus, GR-15773 Athens, Greece

[2] ATHENA RC, ILSP, Maroussi GR-15125, Greece

来源：

RO-MAN 2009: THE 18TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1 AND 2 | 2009年

关键词：

SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents results achieved in the frames of a national research project (titled "DIANOEMA"), where visual analysis and sign recognition techniques have been explored on Greek Sign Language (GSL) data. Besides GSL modelling, the aim was to develop a pilot application for teleoperating a mobile robot using natural hand signs. A small vocabulary of hand signs has been designed to enable desktop-based teleoperation at a high-level of supervisory telerobotic control. Real-time visual recognition of the hand images is performed by training a multi-layer perceptron (MLP) neural network. Various shape descriptors of the segmented hand posture images have been explored as inputs to the MLP network. These include Fourier shape descriptors on the contour of the segmented hand sign images, moments, compactness, eccentricity, and histogram of the curvature. We have examined which of these shape descriptors are best suited for real-time recognition of hand signs, in relation to the number and choice of hand postures, in order to achieve maximum recognition performance. The hand-sign recognizer has been integrated in a graphical user interface, and has been implemented with success on a pilot application for real-time desktop-based gestural teleoperation of a mobile robot vehicle.

引用

页码：700 / +

页数：2

共 21 条

[1]

Argyros A e., 2005, Tracking Skin-colored Objects in Real-time

[2]

Bonato V., 2004, INT C INFORMATICS CO, P207

[3]

Chang CC, 2004, 2004 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXP (ICME), VOLS 1-3, P1187

[4]

Chang CC, 2002, INT C PATT RECOG, P386, DOI 10.1109/ICPR.2002.1048320

[5]

CORRADINI HM, 2000, IEEE INNS ENNS INT J, V4, P4133

[6]

HU A, 2002, P 2003 IEEE INT C IN, P1560

[7] Sign language recognition using model-based tracking and a 3D Hopfield neural network [J].

Huang, CL ;

Huang, WY .

MACHINE VISION AND APPLICATIONS, 1998, 10 (5-6) :292-307

[8]

ISAACS J, 2004, 2004 C EV COMP CEC 2, V1, P797

[9]

JUANG CF, 2005, P 2005 IEEE INT JOIN

[10]

KHEDDAR A, 1997, INT J ADV ROBOTICS, V11, P799

← 1 2 3 →