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Object Tracking Algorithm Based on Dual Color Feature Fusion with Dimension Reduction.

Shuo Hu1, Yanan Ge2, Jianglong Han3

  • 1School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China. hus@ysu.edu.cn.

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|December 27, 2018
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Summary
This summary is machine-generated.

This study introduces a dual color feature fusion object tracking algorithm for complex scenes. The method enhances robustness and effectiveness by combining Color Name (CN) and Color Histogram (CH) features, outperforming existing methods.

Keywords:
correlation filterfeature fusionprincipal component analysisself-adaptive feature fusionvisual tracking

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Single color features limit object tracking robustness in complex scenes.
  • Existing methods struggle with variations in illumination and appearance.

Purpose of the Study:

  • To propose an object tracking algorithm using dual color feature fusion for improved robustness and effectiveness.
  • To address limitations of single color-based tracking in challenging environments.

Main Methods:

  • Employs a Correlation Filter (CF)-based tracking framework.
  • Extracts Color Name (CN) and Color Histogram (CH) features.
  • Applies Principal Component Analysis (PCA) for dimension reduction and fuses CN and CH responses adaptively.

Main Results:

  • The fused response map accurately estimates target position.
  • Achieves improved tracking accuracy and robustness compared to state-of-the-art algorithms.
  • Demonstrates superior performance on challenging benchmark sequences.

Conclusions:

  • Dual color feature fusion significantly enhances object tracking performance in complex scenes.
  • The proposed method offers a robust and effective solution for object tracking.
  • Dimension reduction improves algorithmic efficiency without compromising accuracy.