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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking.

Jianzhong He1, Yuanfa Ji1,2,3, Xiyan Sun1,2,3,4

  • 1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel background-suppressed dual-regression correlation filter (BSDCF) for visual tracking. BSDCF effectively distinguishes targets from backgrounds, improving tracking accuracy in challenging conditions like occlusion and clutter.

Keywords:
background suppresseddiscriminative correlation filterdual regressionresponse aberrationvisual object tracking

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Discriminative Correlation Filter (DCF) trackers offer efficiency but suffer from boundary effects and background interference.
  • Real-world tracking faces challenges like occlusion, clutter, and illumination changes, leading to tracking failures.

Purpose of the Study:

  • To propose a novel tracking method, the background-suppressed dual-regression correlation filter (BSDCF), to overcome limitations of existing DCF trackers.
  • To enhance target discrimination and suppress background interference for robust visual tracking.

Main Methods:

  • Utilized a background-suppressed function to extract target features.
  • Employed spatial regularity constraints and background response suppression regularization during training.
  • Implemented a dual regression structure to train target and global filters separately, using response map differences for mutual constraint.
  • Enhanced detection via weighted fusion of target and global responses.

Main Results:

  • The proposed BSDCF tracker demonstrated improved performance in distinguishing targets from backgrounds.
  • The method effectively suppressed background interference, reducing response aberration.
  • Experimental results on OTB100, TC128, and UAVDT benchmarks showed comparable performance to state-of-the-art trackers.

Conclusions:

  • The BSDCF tracker offers a robust solution for visual tracking in complex environments.
  • The dual regression and background suppression mechanisms contribute to enhanced tracking accuracy and reliability.