Action categorization with modified hidden conditional random field

  • Authors:
  • Jianguo Zhang;Shaogang Gong

  • Affiliations:
  • School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK;Department of Computer Science, Queen Mary University of London, London E1 4NS, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.