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Visual tracking and recognition using appearance-adaptive models in particle filters.

Shaohua Kevin Zhou1, Rama Chellappa, Baback Moghaddam

  • 1University of Maryland, College Park, USA. kzhou@scr.siemens.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 16, 2004
PubMed
Summary

This study introduces adaptive models within particle filters for stable visual tracking and recognition. The approach enhances robustness by dynamically adjusting appearance, motion, and particle count, improving performance in challenging video sequences.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Traditional visual tracking algorithms struggle with instability due to fixed appearance/motion models and particle counts.
  • Interframe motion and appearance changes are critical for tracking, while recognition requires modeling appearance variations between frames and gallery images.
  • Existing methods often feature fixed or rapidly changing appearance models and simplistic motion models, leading to tracker unreliability.

Purpose of the Study:

  • To develop a robust visual tracking and recognition system using appearance-adaptive models within a particle filter framework.
  • To enhance tracker stability by introducing adaptive components for appearance, motion, and particle number.
  • To achieve simultaneous tracking and recognition, addressing challenges like pose and view variations.

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Main Methods:

  • Incorporation of an adaptive appearance model into the observation model of a particle filter.
  • Development of an adaptive velocity motion model with adaptive noise variance, utilizing a first-order linear predictor.
  • Implementation of an adaptive number of particles and occlusion analysis using robust statistics.
  • Simultaneous tracking and recognition achieved by modeling appearance changes using intra- and extrapersonal spaces.

Main Results:

  • Experimental results demonstrate the effectiveness and robustness of the proposed tracking algorithm on long outdoor and indoor video sequences.
  • The adaptive approach significantly stabilizes visual trackers compared to conventional methods.
  • Accurate recognition is achieved despite pose and view variations by modeling appearance changes.

Conclusions:

  • The proposed appearance-adaptive particle filter provides a stable and robust solution for visual tracking and recognition tasks.
  • Dynamic adaptation of appearance, motion, and particle count is crucial for overcoming limitations of conventional visual tracking algorithms.
  • The method shows promise for real-world applications requiring reliable object tracking and identification in complex visual environments.