Sign Language Numeral Gestures Recognition Using Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952760" target="_blank" >RIV/49777513:23520/18:43952760 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-99582-3_8" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-99582-3_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-99582-3_8" target="_blank" >10.1007/978-3-319-99582-3_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sign Language Numeral Gestures Recognition Using Convolutional Neural Network

Popis výsledku v původním jazyce

This paper presents usage of convolutional neural network for classification of sign language numeral gestures. For requirements of this research, we created a new dataset of these gestures. The dataset was recorded via Kinect v2 device and it consists of recordings of 18 different people. Only depth data-stream was used in our research. For a classification task, there was utilized classic VGG16 architecture and its results were compared with chosen baseline method and other tested architectures. Our experiment on classification showed the great potential of neural networks for this task. We reached recognition accuracy 86.45%, which is by more than 34% better result than chosen baseline method.

Název v anglickém jazyce

Sign Language Numeral Gestures Recognition Using Convolutional Neural Network

Popis výsledku anglicky

This paper presents usage of convolutional neural network for classification of sign language numeral gestures. For requirements of this research, we created a new dataset of these gestures. The dataset was recorded via Kinect v2 device and it consists of recordings of 18 different people. Only depth data-stream was used in our research. For a classification task, there was utilized classic VGG16 architecture and its results were compared with chosen baseline method and other tested architectures. Our experiment on classification showed the great potential of neural networks for this task. We reached recognition accuracy 86.45%, which is by more than 34% better result than chosen baseline method.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Interactive Collaborative Robotics Third International Conference, ICR 2018 Leipzig, Germany, September 18–22, 2018 Proceedings

ISBN

978-3-319-99581-6

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

8

Strana od-do

70-77

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Leipzig, Germany

Datum konání akce

18. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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