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Adaptive Probabilistic Visual Tracking with Incremental Subspace Update

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)

Abstract

Visual tracking, in essence, deals with non-stationary data streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail if there is a significant change in object appearance or surrounding illumination. The reason being that these visual tracking algorithms operate on the premise that the models of the objects being tracked are invariant to internal appearance change or external variation such as lighting or viewpoint. Consequently most tracking algorithms do not update the models once they are built or learned at the outset. In this paper, we present an adaptive probabilistic tracking algorithm that updates the models using an incremental update of eigenbasis. To track objects in two views, we use an effective probabilistic method for sampling affine motion parameters with priors and predicting its location with a maximum a posteriori estimate. Borne out by experiments, we demonstrate the proposed method is able to track objects well under large lighting, pose and scale variation with close to real-time performance.

Keywords

Singular Value Decomposition Target Object Training Image Tracking Algorithm Appearance 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 2004

Authors and Affiliations

  1. 1.University of TorontoTorontoCanada
  2. 2.University of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Honda Research InstituteMountain ViewUSA

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