Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

  • Thomas Kopinski
  • Alexander Gepperth
  • Stefan Geisler
  • Uwe Handmann
Conference paper

DOI: 10.1007/978-3-319-11179-7_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)
Cite this paper as:
Kopinski T., Gepperth A., Geisler S., Handmann U. (2014) Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors. In: Wermter S. et al. (eds) Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham

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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Kopinski
    • 1
  • Alexander Gepperth
    • 2
  • Stefan Geisler
    • 1
  • Uwe Handmann
    • 1
  1. 1.Computer Science InstituteUniversity of Applied Sciences BottropMühlheimGermany
  2. 2.ENSTA ParisTech- UIIS Lab, 828 Blvd des MaréchauxPalaiseauFrance

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