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Abstract

This paper presents a novel method for discovering change-points in a time series of elements in the set of rigid-body motion in space SE(3). Although numerous change-points detection techniques are available for dealing with scalar, or vector, time series, the generalization of these techniques to more complex structures may require overcoming difficult challenges. The group SE(3) does not satisfy closure under linear combination. Consequently, most of the statistical properties, such as the mean, cannot be properly estimated in a straightforward manner. We present a method that takes advantage of the Lie group structure of SE(3) to adapt a difference of means method. Especially, we show that the change-point in SE(3) can be discovered in its Lie algebra se(3) that forms a vector space. The performance of our method is evaluated through both synthetic and real-data.

Keywords

Window Size Live Motion Principal Geodesic Analysis Motion Capture Device Social Intelligence Design 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Loic Merckel
    • 1
  • Toyoaki Nishida
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

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