A Deep Learning Approach for Hand Posture Recognition From Depth Data
- Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 %.
Author: | Thomas Kopinski, Fabian Sachara, Alexander Gepperth, Uwe Handmann |
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URL: | https://hal.archives-ouvertes.fr/hal-01418137/document |
Document Type: | Conference Proceeding |
Language: | English |
Year of Completion: | 2016 |
Release Date: | 2019/07/04 |
Institutes: | Fachbereich 1 - Institut Informatik |
DDC class: | 000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft |
Licence (German): | No Creative Commons |