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Student-t Mixture Filter for Robust, Real-Time Visual Tracking

  • James Loxam
  • Tom Drummond
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)

Abstract

Filtering is a key problem in modern information theory; from a series of noisy measurement, one would like to estimate the state of some system. A number of solutions exist in the literature, such as the Kalman filter or the various particle and hybrid filters, but each has its drawbacks.

In this paper, a filter is introduced based on a mixture of Student-t modes for all distributions, eliminating the need for arbitrary decisions when treating outliers and providing robust real-time operation in a true Bayesian manner.

Keywords

Posterior Distribution Heavy Tail Approximate Inference High Order Cumulants Data Confusion 
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.

Supplementary material

978-3-540-88690-7_28_MOESM1_ESM.avi (20.2 mb)
Supplementary material (20,655 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • James Loxam
    • 1
  • Tom Drummond
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeUSA

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