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Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras

  • In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.

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Metadaten
Author:Nico Zengeler, Thomas Kopinski, Uwe Handmann
URL:https://www.researchgate.net/publication/329900072_Hand_Gesture_Recognition_in_Automotive_Human-Machine_Interaction_Using_Depth_Cameras
URL:https://www.mdpi.com/1424-8220/19/1/59/htm
URL:http://www.handmann.net/pdf/SENSORS-ZenKopHan2019pdf
DOI:https://doi.org/10.3390/s19010059
Parent Title (English):sensors
Document Type:Article
Language:English
Year of Completion:2018
Release Date:2019/07/01
Volume:59
Issue:19,(1)
Page Number:27
Institutes:Fachbereich 1 - Institut Energiesysteme und Energiewirtschaft
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International