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Hierarchical Method for Segmentation by Classification of Motion Capture Data

  • Samer SalamahEmail author
  • Liang Zhang
  • Guido Brunnett
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8844)

Abstract

In this paper, we present a novel simple and efficient method for segmentation by classification of motion capture data automatically and with high accuracy. Classification of motion capture data demands dealing with high dimensional search space due to the high dimensionality of the motion capture data. The main contribution of this paper is a method for reducing this search space using the divide and conquer principle in a form of a taxonomy-tree which means a multi-level segmentation by classification algorithm, where the highest level classifies motion capture data into dynamic and static segments and the lowest level uses features of single body-parts to recognize wide range of human movements. The first implementation of this algorithm has given very promising results and proved that it is fast enough to be integrated in real-time systems such as robotics and surveillance systems.

Keywords

Human motion Motion capture Motion segmentation Motion classification Activity recognition 

Notes

Acknowledgments

The data used in this work was obtained from motion capture.cs.cmu.edu.

Supplementary material

Supplementary material 1 (AVI 40089 kb)

336837_1_En_10_MOESM2_ESM.avi (5.7 mb)
Supplementary material 2 (AVI 12119 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer ScienceGDV Chemnitz University of TechnologyChemnitzGermany

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