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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    Title :

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


    Contributors:


    Publication date :

    2015-06-01


    Size :

    877578 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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