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One-Sequence Learning of Human Actions

  • Carlos Orrite
  • Mario Rodríguez
  • Miguel Montañés
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7065)

Abstract

In this paper we address the problem of human action recognition from a single training sequence per class using a modified version of the Hidden Markov Model. Inspired by codebook approaches in object and scene categorization, we first construct a codebook of possible discrete observations by applying a clustering algorithm to all samples from all classes. The number of clusters defines the size of the codebook. Given a new observation, we assign to it a probability to belong to every cluster, i.e., to correspond to a discrete value of the codebook. In this sense, we change the ‘winner takes all’ rule in the discrete-observation HMM for a distributed probability of membership. It implies the modification of the Baum-Welch algorithm for training discrete HMM to be able to deal with fuzzy observations. We compare our approach with other models such as, dynamic time warping (DTW), continuous-observation HMM, Conditional Random Fields (CRF) and Hidden Conditional Random Fields (HCRF) for human action recognition.

Keywords

Hide Markov Model Training Sequence Dynamic Time Warping Conditional Random Field Human 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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos Orrite
    • 1
  • Mario Rodríguez
    • 1
  • Miguel Montañés
    • 1
  1. 1.I3A, University of ZaragozaSpain

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