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CAT: Centerness-Aware Anchor-Free Tracker.

Haoyi Ma1, Scott T Acton1, Zongli Lin1

  • 1Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904-4743, USA.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

A new centerness-aware anchor-free tracker (CAT) improves visual object tracking by eliminating manual parameter tuning. This method enhances accuracy and robustness in diverse tracking scenarios.

Keywords:
anchor-freecenternessconvolutional neural networkvisual object tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Visual object tracking scale estimation is challenging.
  • Existing methods rely on multi-scale searching or anchor boxes with heuristic parameters.
  • These parameters limit flexibility and generality.

Purpose of the Study:

  • Introduce a centerness-aware anchor-free tracker (CAT).
  • Enable accurate and robust scale estimation in visual object tracking.
  • Remove the need for anchor-box-related hyperparameters.

Main Methods:

  • Decompose tracking into parallel classification and regression problems.
  • Employ an anchor-free approach for location and scale prediction.
  • Utilize a centerness-aware classification branch to identify foreground and predict target center distance.

Main Results:

  • The anchor-free design enhances CAT's generality and flexibility.
  • Centerness-aware classification improves tracking accuracy and robustness.
  • CAT achieves salient performance across various tracking scenarios.

Conclusions:

  • CAT offers a more generic and flexible solution for visual object tracking.
  • The centerness-aware approach significantly boosts tracking performance.
  • This method effectively suppresses low-quality state estimates for robust tracking.