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3D Gait Recognition Using Spatio-Temporal Motion Descriptors

  • Bogdan Kwolek
  • Tomasz Krzeszowski
  • Agnieszka Michalczuk
  • Henryk Josinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8398)

Abstract

We present a view independent algorithm for 3D human gait recognition. The identification of the person is achieved using motion data obtained by our markerless 3D motion tracking algorithm. We report its tracking accuracy using ground-truth data obtained by a marker-based motion capture system. The classification is done using SVM built on the proposed spatio-temporal motion descriptors. The identification performance was determined using 230 gait cycles performed by 22 persons. The correctly classified ratio achieved by SVM is equal to 93.5% for rank 1 and 99.6% for rank 3. We show that the recognition performance obtained with the spatio-temporal gait signatures is better in comparison to accuracy obtained with tensorial gait data and reduced by MPCA.

Keywords

Gait Cycle Motion Tracking Motion Capture System Gait Recognition Human Body Model 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Bogdan Kwolek
    • 1
  • Tomasz Krzeszowski
    • 3
  • Agnieszka Michalczuk
    • 2
  • Henryk Josinski
    • 2
  1. 1.AGH University of Science and TechnologyKrakowPoland
  2. 2.Polish-Japanese Institute of Information TechnologyBytomPoland
  3. 3.Rzeszów University of TechnologyRzeszówPoland

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