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Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion.

Tao Wang1, Wen Wang2, Hui Liu3

  • 1Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China. wwwlljwt@163.com.

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Summary

This study introduces a novel algorithm for robust video face tracking using big data. The method enhances accuracy and reliability in complex environments by adaptively adjusting tracking windows and updating templates.

Keywords:
features fusionparticle filter (PF)template driftupdating modelvideo face tracking

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Area of Science:

  • Artificial Intelligence
  • Information Security
  • Computer Vision

Background:

  • Big data and the Internet of Things drive AI research.
  • Face recognition offers unique security advantages.
  • Video face tracking is crucial for information security.

Purpose of the Study:

  • To develop an advanced algorithm for accurate video face tracking in big data environments.
  • To enhance the robustness and precision of face tracking under challenging conditions.

Main Methods:

  • Proposed a multi-feature fusion adaptive adjustment target tracking window.
  • Developed an adaptive update template particle filter tracking framework.
  • Extracted skin color, edge, and weighted color histogram features.
  • Utilized integral histogram for simplified particle calculations.
  • Adjusted tracking window based on average distance changes.

Main Results:

  • The proposed method significantly improves video face tracking performance.
  • Demonstrated strong robustness against skin color variations, illumination changes, and face occlusion.
  • Achieved enhanced accuracy and reliability in complex backgrounds.

Conclusions:

  • The developed algorithm offers a robust and accurate solution for video face tracking in big data.
  • The adaptive template updating mechanism improves tracking precision.
  • The method is well-suited for real-world information security applications.