Robust Multi-hypothesis 3D Object Pose Tracking

  • Georgios Chliveros
  • Maria Pateraki
  • Panos Trahanias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7963)


This paper tackles the problem of 3D object pose tracking from monocular cameras. Data association is performed via a variant of the Iterative Closest Point algorithm, thus making it robust to noise and other artifacts. We re-initialise the hypothesis space based on the resulting re-projection error between hypothesised models and observed image objects. This is performed through a non-linear minimisation step after correspondences are found. The use of multi-hypotheses and correspondences refinement, lead to a robust framework. Experimental results with benchmark image sequences indicate the effectiveness of our framework.


Robot Vision Object Tracking Relative Pose Estimation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georgios Chliveros
    • 1
  • Maria Pateraki
    • 1
  • Panos Trahanias
    • 1
    • 2
  1. 1.Foundation for Research and Technology Hellas, Institute of Computer ScienceHeraklionGreece
  2. 2.Dept. of Computer ScienceUniversity of CreteHeraklionGreece

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