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Related Experiment Video

Updated: May 23, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Transferring visual prior for online object tracking.

Qing Wang1, Feng Chen, Jimei Yang

  • 1National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China. qing-wang07@mails.tsinghua.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 12, 2012
PubMed
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This study introduces a novel algorithm for online object tracking by transferring visual prior learned from generic images. This method enhances tracking robustness by representing objects using sparse coding and multiscale pooling.

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Visual prior from generic images can represent objects.
  • Transferring learned visual prior for online object tracking is proposed.

Purpose of the Study:

  • To develop an algorithm that transfers offline-learned visual prior for online object tracking.
  • To improve object tracking robustness by leveraging generic visual knowledge.

Main Methods:

  • Learned an overcomplete dictionary from real-world images to represent visual prior.
  • Transferred visual prior using sparse coding and multiscale max pooling for object representation.
  • Employed a linear classifier for online target-background distinction and an adaptive observation model within a Bayesian inference framework using a particle filter.

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Last Updated: May 23, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Published on: January 18, 2020

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Main Results:

  • The proposed algorithm demonstrated robust object tracking on challenging sequences.
  • Experimental comparisons showed superior performance against state-of-the-art methods.

Conclusions:

  • Transferring visual prior significantly enhances the robustness of online object tracking.
  • The method effectively handles appearance variations of targets and backgrounds over time.