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Recognition of Human Actions from RGB-D Videos Using a Reject Option

  • Vincenzo Carletti
  • Pasquale Foggia
  • Gennaro Percannella
  • Alessia Saggese
  • Mario Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)

Abstract

In this paper we propose a method for recognizing human actions by using depth images acquired through a Kinect sensor. The depth images are represented through the combination of three sets of well-known features, respectively based on Hu moments, depth variations and the \(\mathfrak{R}\) transform, an enhanced version of the Radon transform. A GMM classifier is adopted and finally a reject option is introduced in order to improve the overall reliability of the system. The proposed approach has been tested over two datasets, the Mivia and the MHAD, showing very promising results.

Keywords

Gaussian Mixture Model Depth Image Kinect Sensor Motion History Image Gaussian Mixture Model Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vincenzo Carletti
    • 1
  • Pasquale Foggia
    • 1
  • Gennaro Percannella
    • 1
  • Alessia Saggese
    • 1
  • Mario Vento
    • 1
  1. 1.Dept. of Information Eng., Electrical Eng. and Applied Mathematics (DIEM)University of SalernoItaly

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