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Action Recognition with HOG-OF Features

  • Florian Baumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)

Abstract

In this paper a simple and efficient framework for single human action recognition is proposed. In two parallel processing streams, motion information and static object appearances are gathered by introducing a frame-by-frame learning approach. For each processing stream a Random Forest classifier is separately learned. The final decision is determined by combining both probability functions. The proposed recognition system is evaluated on the KTH data set for single human action recognition with original training/testing splits and a 5-fold cross validation. The results demonstrate state-of-the-art accuracies with an overall training time of 30 seconds on a standard workstation.

Keywords

Human Action Recognition Human Detection Oriented Gradient British Machine Vision Conference Standard Workstation 
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

  • Florian Baumann
    • 1
  1. 1.Institut für InformationsverarbeitungLeibniz Universität HannoverGermany

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