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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Updated: Aug 23, 2025

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A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object.

Kaiyuan Zheng1, Zhiyong Zhang1, Changzhen Qiu1

  • 1School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a fast multi-scale kernel correlation filter tracker for real-time object tracking in complex scenes. The adaptive template updating method effectively handles scale changes and occlusion, outperforming existing kernel correlation filter trackers.

Keywords:
adaptive template updaterkernel correlation filterrigid objecttarget tracking

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Deep learning models offer high tracking accuracy but struggle with real-time performance on limited hardware.
  • Kernel Correlation Filter (KCF) trackers are fast but fail to adapt to scale changes and occlusion, leading to template drift.

Purpose of the Study:

  • To develop a real-time object tracking algorithm for complex scenes on resource-constrained platforms.
  • To improve the robustness of Kernel Correlation Filter trackers against scale variations and occlusions.

Main Methods:

  • A fast multi-scale kernel correlation filter tracker incorporating a scale pyramid for scale adaptation.
  • An adaptive template updater using Mean of Cumulative Maximum Response Values (MCMRV) to mitigate template drift during occlusion.

Main Results:

  • The proposed tracker effectively adapts to target scale changes while maintaining high operational speed.
  • The MCMRV-based adaptive template updater significantly reduces template drift in the presence of occlusion.
  • Experimental results show superior performance compared to state-of-the-art kernel correlation filter methods on various datasets.

Conclusions:

  • The proposed fast multi-scale kernel correlation filter tracker offers an effective solution for real-time object tracking in challenging environments.
  • The adaptive template updating mechanism enhances tracker robustness, making it suitable for embedded systems with limited computational resources.