Fourier Features For Person Detection in Depth Data

  • Viktor SeibEmail author
  • Guido Schmidt
  • Michael Kusenbach
  • Dietrich Paulus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


A robust and reliable person detection is crucial for many applications. In the domain of service robots that we focus on, knowing the location of a person is an essential requirement for any meaningful human-robot interaction. In this work we present a people detection algorithm exploiting RGB-D data from Kinect-like cameras. Two features are obtained from the data representing the geometrical properties of a person. These features are transformed into the frequency domain using Discrete Fourier Transform (DFT) and used to train a Support Vector Machine (SVM) for classification. Additionally, we present a hand detection algorithm based on the extracted silhouette of a person. We evaluate the proposed method on real world data from the Cornell Activity Dataset and on a dataset created in our laboratory.


People detection Silhouette detection Hand detection Fourier features Service robots 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Viktor Seib
    • 1
    Email author
  • Guido Schmidt
    • 1
  • Michael Kusenbach
    • 1
  • Dietrich Paulus
    • 1
  1. 1.Active Vision Group (AGAS)University of Koblenz-LandauKoblenzGermany

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