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Human Action Recognition in Table-Top Scenarios : An HMM-Based Analysis to Optimize the Performance

  • Pradeep Reddy Raamana
  • Daniel Grest
  • Volker Krueger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Hidden Markov models have been extensively and successfully used for the recognition of human actions. Though there exist well-established algorithms to optimize the transition and output probabilities, the type of features to use and specifically the number of states and Gaussian have to be chosen manually. Here we present a quantitative study on selecting the optimal feature set for recognition of simple object manipulation actions pointing, rotating and grasping in a table-top scenario. This study has resulted in recognition rate higher than 90%. Also three different parameters, namely the number of states and Gaussian for HMM and the number of training iterations, are considered for optimization of the recognition rate with 5 different feature sets on our motion capture data set from 10 persons.

Keywords

Hidden Markov model Action Recognition Optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pradeep Reddy Raamana
    • 1
  • Daniel Grest
    • 1
  • Volker Krueger
    • 1
  1. 1.Computer Vision and Machine Intelligence, Copenhagen Institute of Technology, Aalborg UniversityDenmark

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