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MoCap Data Segmentation and Classification Using Kernel Based Multi-channel Analysis

  • Sergio García-Vega
  • Andrés Marino Álvarez-Meza
  • César Germán Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

A methodology for automatic segmentation and classification of multi-channel data related to motion capture (MoCap) videos of cyclic activities are presented. Regarding this, a kernel approach is employed to obtain a time representation, which captures the cyclic behavior of a given multi-channel data. Moreover, we calculate a mapping based on kernel principal component analysis, in order to obtain a low-dimensional space that encodes the main cyclic behaviors. From such, low-dimensional space the main segments of the studied activity are inferred. Then, a distance based classifier is used to classified each MoCap video segment. A well-known MoCap database is tested which contains different activities performed by humans. Attained results shows how our approach is a simple alternative to obtain a suitable classification performance in comparison to complex methods for MoCap analysis.

Keywords

Multi-channel data kernel methods MoCap human activity recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sergio García-Vega
    • 1
  • Andrés Marino Álvarez-Meza
    • 1
  • César Germán Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de Colombia, Sede ManizalesColombia

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