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Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

Abdel Hamid Mbouombouo Mboungam1, Yongfeng Zhi1, Cedric Karel Fonzeu Monguen2

  • 1School of Automation, Northwestern Polytechnical University, Xi'an 710002, China.

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Summary
This summary is machine-generated.

This study enhances real-time object tracking using kernelized correlation filters (KCFs) by introducing scale pyramids and improved drift suppression for greater accuracy and resilience in complex environments.

Keywords:
kernelized correlation filtermodel driftobject detectionreal-time object trackingvariance classifier

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Real-time object tracking is vital for image-guided technologies but faces challenges in complex environments.
  • Existing nuclear correlation tracking algorithms balance speed and performance but struggle with model drift and scale variation.

Purpose of the Study:

  • To develop an improved object tracking method addressing limitations of kernelized correlation filters (KCFs).
  • To enhance real-time tracking accuracy and robustness in challenging scenarios.

Main Methods:

  • Kernelized correlation filters (KCFs) were researched, including model training and dense sampling.
  • A scale pyramid approach was developed to handle target scale transformation.
  • Improvements to KCFs included an initial object model for drift suppression and an object detection module for failure recovery.

Main Results:

  • The scale pyramid approach effectively resolved target size variation issues.
  • The improved KCF demonstrated reduced model drift and enhanced long-term tracking capabilities.
  • An evaluation system confirmed the proposed method's superior accuracy and resilience compared to standard KCF and other tracking methods.

Conclusions:

  • The proposed enhanced kernelized correlation filter method significantly improves real-time object tracking performance.
  • The integrated scale pyramid and drift suppression techniques provide a robust solution for complex tracking tasks.
  • This work offers a valuable advancement for image-guided technologies requiring reliable object tracking.