A light-weight real-time applicable hand gesture recognition system for automotive applications

Thomas Kopinski 1 Stéphane Magand 2 Alexander Gepperth 3, 2 Uwe Handmann 1
1 HRW - Hochschule Ruhr West
2 RV - Robotique et Vision
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : We present a novel approach for improved hand-gesture recognition by a single time-of-flight(ToF) sensor in an automotive environment. As the sensor's lateral resolution is comparatively low, we employ a learning approach comprising multiple processing steps, including PCA-based cropping, the computation of robust point cloud descriptors and training of a Multilayer perceptron (MLP) on a large database of samples. A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps. Overall results are very satisfactory when evaluated on a large benchmark set of ten different hand poses, especially when it comes to generalization on previously unknown persons.
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Thomas Kopinski, Stéphane Magand, Alexander Gepperth, Uwe Handmann. A light-weight real-time applicable hand gesture recognition system for automotive applications. IEEE International Symposium on Intelligent Vehicles (IV), Jun 2015, Seoul, South Korea. pp.336-342, ⟨10.1109/IVS.2015.7225708⟩. ⟨hal-01251413⟩

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