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Behavior Histograms for Action Recognition and Human Detection

  • Conference paper
Book cover Human Motion – Understanding, Modeling, Capture and Animation (HuMo 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4814))

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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.

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Author information

Authors and Affiliations

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

    Christian Thurau

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  1. Christian Thurau
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Editor information

Ahmed Elgammal Bodo Rosenhahn Reinhard Klette

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© 2007 Springer-Verlag Berlin Heidelberg

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Thurau, C. (2007). Behavior Histograms for Action Recognition and Human Detection. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds) Human Motion – Understanding, Modeling, Capture and Animation. HuMo 2007. Lecture Notes in Computer Science, vol 4814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75703-0_21

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  • DOI: https://doi.org/10.1007/978-3-540-75703-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75702-3

  • Online ISBN: 978-3-540-75703-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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