Chapter

Vision Based Systemsfor UAV Applications

Volume 481 of the series Studies in Computational Intelligence pp 233-245

Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification

  • Henryk JosińskiAffiliated withInstitute of Computer Science, Silesian University of Technology Email author 
  • , Daniel KostrzewaAffiliated withInstitute of Computer Science, Silesian University of Technology
  • , Agnieszka MichalczukAffiliated withInstitute of Computer Science, Silesian University of Technology
  • , Adam ŚwitońskiAffiliated withInstitute of Computer Science, Silesian University of Technology
  • , Konrad WojciechowskiAffiliated withInstitute of Computer Science, Silesian University of Technology

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Abstract

The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).

Keywords

dimensionality reduction gait-based human identification Hidden Markov model manifold learning