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A Deep Learning Approach to Mid-air Gesture Interaction for Mobile Devices from Time-of-Flight Data (MOBIQUITOUS 2016)

  • This contribution presents a novel approach of utilizing Time-of-Flight (ToF) technology for mid-air hand gesture recognition on mobile devices. ToF sensors are capable of providing depth data at high frame rates independent of illumination making any kind of application possible for in- and outdoor situations. This comes at the cost of precision regarding depth measurements and comparatively low lateral resolution. We present a novel feature generation technique based on a rasterization of the point clouds which realizes fixed-sized input making Deep Learning approaches applicable using Convolutional Neural Networks. In order to increase precision we introduce several methods to reduce noise and normalize the input to overcome difficulties in scaling. Backed by a large-scale database of about half a million data samples taken from different individuals our contribution shows how hand gesture recognition is realiz- able on commodity tablets in real-time at frame rates of up to 17Hz. A leave-one out cross-validation experiment demonstrates the feasibility of our approach with classification errors as low as 1,5% achieved persons unknown to the model.

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Author:Thomas Kopinski, Fabian Sachara, Alexander Gepperth, Uwe Handmann
Document Type:Conference Proceeding
Year of Completion:2016
Release Date:2019/07/05
Page Number:9
First Page:1
Last Page:9
Institutes:Fachbereich 1 - Institut Informatik
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):License LogoNo Creative Commons