@inproceedings{KopinskiMagandGepperthetal.2015,
  author    = {Thomas Kopinski and St{\´e}phane Magand and Alexander Gepperth and Uwe Handmann},
  title     = {A light-weight real-time ap- plicable hand gesture recognition system for automotive applications},
  series = {IEEE Intelligent Vehicles Symposiume (IV 2015)},
  volume    = {2015},
  publisher = {IEEE},
  isbn      = {978-1-4673-7266-4},
  issn      = {1931-0587},
  doi       = {10.1109/IVS.2015.7225708},
  pages     = {336 -- 342},
  year      = {2015},
  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.},
  language  = {en}
}