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Robust Auxiliary Particle Filter with an Adaptive Appearance Model for Visual Tracking

  • Du Yong Kim
  • Ehwa Yang
  • Moongu Jeon
  • Vladimir Shin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined.

Keywords

Visual Tracking Slide Window Approach Auxiliary Particle Abrupt Motion Observation Likelihood 
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 2011

Authors and Affiliations

  • Du Yong Kim
    • 1
  • Ehwa Yang
    • 1
  • Moongu Jeon
    • 1
  • Vladimir Shin
    • 1
  1. 1.School of Information and MechatronicsGwangju Institute of Science and TechnologyKorea

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