A real-time applicable dynamic hand gesture recognition framework

  • We present a system for efficient dynamic hand gesture recognition based on a single time-of-flight sensor. As opposed to other approaches, we simply rely on depth data to interpret user movement with the hand in mid-air. We set up a large database to train multilayer perceptrons (MLPs) which are subsequently used for classification of static hand poses that define the targeted dynamic gestures. In order to remain robust against noise and to balance the low sensor resolution, PCA is used for data cropping and highly descriptive features, obtainable in real-time, are presented. Our simple yet efficient definition of a dynamic hand gesture shows how strong results are achievable in an automotive environment allowing for interesting and sophisticated applications to be realized.

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Metadaten
Author:Thomas Kopinski, Uwe Handmann, Alexander Gepperth
URL:https://ieeexplore.ieee.org/document/7313473
DOI:https://doi.org/10.1109/ITSC.2015.381
ISBN:978-1-4673-6596-3
ISSN:2153-0017
Parent Title (English):IEEE 18th International Conference on Intelligent Trans- portation Systems (ITSC 2015)
Document Type:Conference Proceeding
Language:English
Year of Completion:2015
Release Date:2019/07/08
Volume:2015
Pagenumber:5
First Page:2358
Last Page:2363
Institutes:Fachbereich 1 - Institut Informatik
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):License LogoNo Creative Commons