Demonstrator für ein handgestenbasiertes Interaktionskonzept im Automobil

Kopinski, Thomas; Geisler, Stefan; Handmann, Uwe

doi:10.1524/9783110344509.205

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this review, we examined current state-of-the-art deep learning technologies for hand gesture recogniton and consolidated a line of research from the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Kopinski’s contributions [34,36,37,41,42,43,44,62,66,73,74,75,76,77,78,79] and PhD thesis [72] form the basis of our hand gesture recognition research. We investigated deep learning technologies for the purpose of hand gesture recognition in automotive context with three-dimensional data from time-of-flight infrared sensors in order to provide new means of controls for driver assistance systems.…”

Section: Discussionmentioning

confidence: 99%

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

Zengeler

Kopinski

Handmann

2018

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract