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Behavior Histograms for Action Recognition and Human Detection

  • Christian Thurau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)

Abstract

This paper presents an approach for human detection and simultaneous behavior recognition from images and image sequences. An action representation is derived by applying a clustering algorithm to sequences of Histogram of Oriented Gradient (HOG) descriptors of human motion images. For novel image sequences, we first detect the human by matching extracted descriptors with the prototypical action primitives. Given a sequence of assigned action primitives, we can build a histogram from observed motion. Thus, behavior can be classified by means of histogram comparison, interpreting behavior recognition as a problem of statistical sequence analysis. Results on publicly available benchmark-data show a high accuracy for action recognition.

Keywords

Recognition Rate IEEE Computer Society Action Recognition Detector Window Human Detection 
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 2007

Authors and Affiliations

  • Christian Thurau
    • 1
  1. 1.Czech Technical University, Faculty of Electrical Engineering, Department for Cybernetics, Center for Machine Perception, 121 35 Prague 2, Karlovo náměstíCzech Republic

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