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Time-of-flight based multi- sensor fusion strategies for hand gesture recognition

  • Building upon prior results, we present an alternative approach to efficiently classifying a complex set of 3D hand poses obtained from modern Time-Of-Flight-Sensors (TOF). We demonstrate it is possible to achieve satisfactory results in spite of low resolution and high noise (inflicted by the sensors) and a demanding outdoor environment. We set up a large database of pointclouds in order to train multilayer perceptrons as well as support vector machines to classify the various hand poses. Our goal is to fuse data from multiple TOF sensors, which observe the poses from multiple angles. The presented contribution illustrates that real-time capability can be maintained with such a setup as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.

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Author:Thomas Kopinski, Darius Maysiak, Alexander Gepperth, Uwe Handmann
Parent Title (English):15th International Symposium on Computational Intelligence and Informatics (CINTI)
Document Type:Conference Proceeding
Year of Completion:2014
Release Date:2019/07/04
Page Number:6
First Page:243
Last Page:248
Institutes:Fachbereich 1 - Institut Informatik
DDC class:600 Technik, Medizin, angewandte Wissenschaften / 621.3 Elektrotechnik, Elektronik
Licence (German):License LogoNo Creative Commons