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Learning dynamic spatial-temporal regularized correlation filter tracking with response deviation suppression via

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Summary
This summary is machine-generated.

This study introduces dynamic spatial-temporal regularization for visual object tracking (VOT) using discriminative correlation filters (DCF). The new method improves tracking accuracy and robustness in challenging scenarios by adapting regularization parameters.

Keywords:
Correlation filtersFeature fusionResponse deviation-suppressionSpatial–temporal informationVisual tracking

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Video Surveillance

Background:

  • Visual object tracking (VOT) is crucial for intelligent video surveillance.
  • Discriminative Correlation Filter (DCF) trackers offer high accuracy and efficiency.
  • Existing DCF methods struggle with fixed regularization parameters in cluttered environments.

Purpose of the Study:

  • To develop a more flexible and adaptive DCF tracking model.
  • To enhance robustness and accuracy in challenging visual tracking scenarios.
  • To introduce dynamic regularization and temporal consistency mechanisms.

Main Methods:

  • Proposed a dynamic spatial-temporal regularization approach for DCF.
  • Introduced a response deviation-suppressed regularization term for temporal consistency.
  • Implemented a multi-memory framework to leverage diverse features.

Main Results:

  • The proposed method demonstrated superior performance compared to state-of-the-art trackers.
  • Achieved higher tracking accuracy and success rates across multiple benchmark datasets.
  • Effectively handled cluttered and challenging tracking scenarios.

Conclusions:

  • Dynamic spatial-temporal regularization significantly enhances DCF tracker performance.
  • The proposed methods improve robustness and adaptability in visual object tracking.
  • This work advances the state-of-the-art in intelligent video surveillance systems.