TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kopinski, Thomas
A1  - Sachara, Fabian
A1  - Gepperth, Alexander
A1  - Handmann, Uwe
T1  - A Deep Learning Approach to Mid-air Gesture Interaction for Mobile Devices from Time-of-Flight Data (MOBIQUITOUS 2016)
N2  - 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.
Y1  - 2016
UR  - http://www.handmann.net/pdf/MOBIQUITOUS-KopHanEtAl2016.pdf
U6  - https://doi.org/10.1145/2994374.2994392
DO  - https://doi.org/10.1145/2994374.2994392
VL  - 2016
SP  - 1
EP  - 9
S1  - 9
ER  -