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Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

  • We present a study on 3D based hand pose recognition using a new generation of low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. We investigate the performance of different 3D descriptors, as well as the fusion of two ToF sensor streams. By basing a data fusion strategy on the fact that multilayer perceptrons can produce normalized confidences individually for each class, and similarly by designing information-theoretic online measures for assessing confidences of decisions, we show that appropriately chosen fusion strategies can improve overall performance to a very satisfactory level. Real-time capability is retained as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.

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Metadaten
Author:Thomas Kopinski, Alexander Gepperth, Uwe Handmann, Stefan Geisler
URL:http://www.handmann.net/pdf/ICANN-KopHanEtAl2014.pdf
URL:https://link.springer.com/chapter/10.1007/978-3-319-11179-7_30
URL:https://hal.inria.fr/hal-01098697/document
DOI:https://doi.org/10.1007/978-3-319-11179-7_30
ISBN:978-3-319-11179-7
Parent Title (German):Artificial Neural Networks and Machine Learning – ICANN 2014
Publisher:Springer
Document Type:Part of a Book
Language:English
Year of Completion:2014
Release Date:2019/07/12
Issue:vol 8681
Pagenumber:8
First Page:233
Last Page:240
Institutes:Fachbereich 1 - Institut Informatik
DDC class:600 Technik, Medizin, angewandte Wissenschaften / 621.3 Elektrotechnik, Elektronik
Licence (German):License LogoNo Creative Commons