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A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter.

Yuanping Zhang1, Xiumei Huang1, Ming Yang1

  • 1College of Computer & Information Science, Southwest University, Chongqing 400715, China.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel long-term visual tracking algorithm using self-organization mapping (SOM) networks and adaptive correlation filters. The method enhances tracking robustness against occlusion and scale changes, outperforming existing approaches.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video target tracking presents challenges due to varying object appearances and environmental conditions.
  • Existing methods struggle with long-term tracking, occlusion, and significant scale variations.
  • Adaptive and unsupervised feature learning is crucial for robust visual tracking.

Purpose of the Study:

  • To propose a long-term visual tracking algorithm that addresses challenges like occlusion, target loss, and scale change.
  • To leverage self-organization mapping (SOM) networks for adaptive, unsupervised feature learning.
  • To develop a robust tracking method using multiple adaptive correlation filters with memory.

Main Methods:

  • Utilized a self-organization mapping (SOM) neural network for adaptive and unsupervised feature learning.
Keywords:
correlation filterdeep learningself-organization mapping networkvisual tracking

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  • Implemented multiple adaptive correlation filters, including displacement, scale, and memory filters.
  • Integrated an incremental learning detector with a sliding window for target re-acquisition upon tracking failure.

    • Main Results:

    • The proposed method demonstrated effective tracking through severe occlusion and scale changes.
    • Experimental results show superiority over state-of-the-art methods in efficiency, accuracy, and robustness.
    • The cooperative strategy of filters with different updating mechanisms ensured reliable long-term tracking.

    Conclusions:

    • The combined SOM network and adaptive correlation filter approach provides a robust solution for long-term visual target tracking.
    • The method effectively handles complex tracking scenarios, including target loss and appearance variations.
    • This algorithm offers significant improvements in performance metrics compared to current leading tracking techniques.