@incollection{KopinskiGepperthHandmannetal.2014,
  author    = {Thomas Kopinski and Alexander Gepperth and Uwe Handmann and Stefan Geisler},
  title     = {Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors},
  series = {Artificial Neural Networks and Machine Learning – ICANN 2014},
  number    = {vol 8681},
  publisher = {Springer},
  isbn      = {978-3-319-11179-7},
  doi       = {10.1007/978-3-319-11179-7\_30},
  pages     = {233 -- 240},
  year      = {2014},
  abstract  = {We present a study on 3D based hand pose recognition using a new generation of low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. We investigate the performance of different 3D descriptors, as well as the fusion of two ToF sensor streams. By basing a data fusion strategy on the fact that multilayer perceptrons can produce normalized confidences individually for each class, and similarly by designing information-theoretic online measures for assessing confidences of decisions, we show that appropriately chosen fusion strategies can improve overall performance to a very satisfactory level. Real-time capability is retained as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.},
  language  = {en}
}