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Updated: Aug 31, 2025

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A practical evaluation of correlation filter-based object trackers with new features.

Islam Mohamed1,2, Ibrahim Elhenawy1, Ahmed W Sallam2

  • 1Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.

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|August 25, 2022
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Summary

This study evaluates Correlation Filter (CF)-based visual object trackers using standard and novel challenging datasets. Results show current trackers struggle with complex sequences, highlighting the need for improved algorithms for real-world applications like Unmanned Aerial Vehicle (UAV) tracking.

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Area of Science:

  • Computer Vision
  • Object Tracking

Background:

  • Correlation Filter (CF)-based trackers offer real-time performance crucial for online applications.
  • Existing datasets lack sequences with combined background clutter, occlusion, and high object displacement challenges.

Purpose of the Study:

  • To introduce novel video sequences for evaluating visual object trackers under challenging conditions.
  • To conduct a comprehensive evaluation of eight top-performing CF-based trackers on standard and new datasets.

Main Methods:

  • Development of two new video sequences featuring difficult-to-track amoeba movements with combined clutter and occlusion.
  • Increased object displacement in existing sequences to enhance tracking difficulty.
  • Performance evaluation of eight CF-based trackers using precision and success rates on OTB-2013 and proposed datasets.

Main Results:

  • On standard datasets, the Large Displacement Estimation of Similarity transformation (LDES) tracker showed top performance.
  • On the proposed challenging sequences, all eight trackers exhibited average performance, failing to consistently track objects.
  • The proposed sequences revealed limitations in current CF-based trackers' ability to handle complex visual features.

Conclusions:

  • Current CF-based object trackers require significant improvement to address sequences with combined background clutter, occlusion, and high displacement.
  • The developed 'hard-to-follow-by-human' sequences are valuable for benchmarking future advancements in visual object tracking.