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Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

Yuankun Li1, Tingfa Xu2,3, Honggao Deng4,5

  • 1School of Optics and Photonics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China. liyuankunbixian@gmail.com.

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Summary
This summary is machine-generated.

This study introduces a keypoints matching strategy to improve correlation filter (CF)-based visual tracking. The enhanced method dynamically adjusts model learning rates, boosting performance in challenging scenarios like occlusion and scale variation.

Keywords:
adaptive model updatingcorrelation filter-based visual trackingdeep convolutional featuredeep convolutional neural networkkeypoints matching

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Correlation filter (CF)-based visual tracking algorithms face challenges with target occlusion and scale variation.
  • Existing CF methods can suffer from model contamination due to learning non-target or partial-target information.

Purpose of the Study:

  • To enhance the adaptability and robustness of CF-based visual trackers.
  • To mitigate model contamination and improve performance in challenging tracking scenarios.

Main Methods:

  • Introduced a keypoints matching strategy to dynamically adjust the model learning rate based on matching scores.
  • Integrated deep convolutional neural network (DCNN) features for accurate target position and scale estimation.

Main Results:

  • The proposed tracker demonstrated satisfactory performance across various challenging visual tracking scenarios.
  • The keypoints matching strategy and dynamic learning rate adjustment effectively addressed model contamination issues.

Conclusions:

  • The novel approach significantly improves the reliability of CF-based visual tracking, particularly under occlusion and scale variations.
  • The integration of DCNN features and adaptive learning rates offers a robust solution for complex tracking tasks.