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Erschienen in:
3D Depth Cameras in Vision: Benefits and Limitations of the Hardware Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

11. Feature Descriptors for Depth-Based Hand Gesture Recognition

verfasst von : Fabio Dominio, Giulio Marin, Mauro Piazza, Pietro Zanuttigh

Erschienen in: Computer Vision and Machine Learning with RGB-D Sensors

Verlag: Springer International Publishing

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Abstract

Depth data acquired by consumer depth cameras provide a very informative description of the hand pose that can be exploited for accurate gesture recognition. A typical hand gesture recognition pipeline requires to identify the hand, extract some relevant features and exploit a suitable machine learning technique to recognize the performed gesture. This chapter deals with the recognition of static poses. It starts by describing how the hand can be extracted from the scene exploiting depth and color data. Then several different features that can be extracted from the depth data are presented. Finally, a multi-class support vector machines (SVM) classifier is applied to the presented features in order to evaluate the performance of the various descriptors.

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Vorheriges Kapitel RGB-D Human Identification and Tracking in a Smart Environment
Nächstes Kapitel Hand Parsing and Gesture Recognition with a Commodity Depth Camera
Fußnoten
1
In Eqs. (11.3) and (11.4) \(L\) is considered as a periodic function with period \(2\pi \).
 
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Metadaten
Titel
Feature Descriptors for Depth-Based Hand Gesture Recognition
verfasst von
Fabio Dominio
Giulio Marin
Mauro Piazza
Pietro Zanuttigh
Copyright-Jahr
2014
Buch
Computer Vision and Machine Learning with RGB-D Sensors

Print ISBN: 978-3-319-08650-7

Electronic ISBN: 978-3-319-08651-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-08651-4_11

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