Tracking the Pose of Objects through Subspace

  • Simon Léonard
  • Martin Jägersand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


Tracking the pose of an object is a fundamental operation in computer vision. Yet, achieving this task for arbitrary objects without requiring a priori knowledge remains a major stumbling block. This paper introduces a method for tracking the pose of a moving object without requiring its 3D model or textured surfaces. In the first step, a sequence of images-poses pairs is obtained and PCA coefficients are derived from the image sequence. Then, a piecewise linear observation mapping is build between the poses and the PCA coefficients. The mapping is then used in the observation model of a Kalman filter that tracks the pose of the object.


Computer Vision Kalman Filter Computer Graphic Texture Surface Observation 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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Simon Léonard
    • 1
  • Martin Jägersand
    • 1
  1. 1.University of AlbertaEdmontonCanada

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