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Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers

  • Qian Yu
  • Thang Ba Dinh
  • Gérard Medioni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

Visual tracking is a challenging problem, as an object may change its appearance due to viewpoint variations, illumination changes, and occlusion. Also, an object may leave the field of view and then reappear. In order to track and reacquire an unknown object with limited labeling data, we propose to learn these changes online and build a model that describes all seen appearance while tracking. To address this semi-supervised learning problem, we propose a co-training based approach to continuously label incoming data and online update a hybrid discriminative generative model. The generative model uses a number of low dimension linear subspaces to describe the appearance of the object. In order to reacquire an object, the generative model encodes all the appearance variations that have been seen. A discriminative classifier is implemented as an online support vector machine, which is trained to focus on recent appearance variations. The online co-training of this hybrid approach accounts for appearance changes and allows reacquisition of an object after total occlusion. We demonstrate that under challenging situations, this method has strong reacquisition ability and robustness to distracters in background.

Keywords

Unlabeled Data Discriminative Model Appearance Change Appearance Variation Generative Tracker 
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 2008

Authors and Affiliations

  • Qian Yu
    • 1
  • Thang Ba Dinh
    • 1
  • Gérard Medioni
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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