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HMM-Based Action Recognition Using Contour Histograms

  • M. Ángeles Mendoza
  • Nicolás Pérez de la Blanca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

This paper describes an experimental study about a robust contour feature (shape-context) for using in action recognition based on continuous hidden Markov models (HMM). We ran different experimental setting using the KTH’s database of actions. The image contours are extracted using a standard algorithm. The shape-context feature vector is build from of histogram of a set ofnon-overlapping regions in the image. We show that the combined use of HMM and this feature gives equivalent o better results, in term of action detection, that current approaches in the literature.

Keywords

Hide Markov Model Discrete Cosine Transform Recognition Rate Optical Flow Action Recognition 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • M. Ángeles Mendoza
    • 1
  • Nicolás Pérez de la Blanca
    • 1
  1. 1.University of Granada, Department of Computer Science and Artificial Intelligence 

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