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Scribble tracker: a matting-based approach for robust tracking.

Jialue Fan1, Xiaohui Shen, Ying Wu

  • 1Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA. jialue.fan@northwestern.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 30, 2012
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel framework combining matting and tracking to prevent model drift in visual tracking. Accurate target boundaries are achieved, significantly improving tracking performance and handling occlusion.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Model updating is crucial for visual tracking, but inaccurate foreground/background extraction causes model drift and performance degradation.
  • Obtaining precise target boundaries is a direct but challenging solution to mitigate tracking model drift.

Purpose of the Study:

  • To propose a novel model adaptation framework integrating matting and tracking to address the challenge of model drift.
  • To achieve accurate target boundary extraction for robust visual tracking, even with significant target deformation.

Main Methods:

  • Developed a framework where coarse tracking results generate scribbles for matting, enabling its application within a tracking system.
  • Constructed an effective model integrating short-term and long-term features, updated using accurate boundaries derived from matting.

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  • Incorporated explicit inference for handling occlusions within the tracking model.

    • Main Results:

    • The proposed framework successfully obtains accurate target boundaries via matting, even with large target deformations.
    • The model adaptation scheme effectively avoids model drift, a common issue in visual tracking.
    • The integrated approach significantly outperforms existing discriminative tracking models in extensive experiments.

    Conclusions:

    • Combining matting and tracking provides an effective solution to model drift in visual tracking systems.
    • Accurate boundary extraction through matting enhances the robustness and performance of visual tracking models.
    • The proposed framework demonstrates superior performance and robustness in handling challenging tracking scenarios, including occlusion.