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A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location.

Yiru Jiang1, Dezhi Han1, Mingming Cui1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

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|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel video target tracking system using feature location and blockchain technology for enhanced accuracy, especially for occluded and small targets. The system achieves superior performance in tracking and stability across datasets.

Keywords:
decentralized surveillanceprivacy-preserving target trackingsecure tracking of objectstarget correctiontrajectory optimization

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Traditional video target tracking struggles with occlusions and small targets, leading to reduced accuracy.
  • Existing methods lack robust mechanisms for secure and decentralized task management in tracking applications.

Purpose of the Study:

  • To propose a cutting-edge video target tracking system integrating advanced feature location with blockchain technology.
  • To enhance tracking accuracy for occluded and small targets through decentralized task organization and adaptive clustering.
  • To improve overall tracking stability and robustness using trajectory optimization post-processing.

Main Methods:

  • Utilizing feature registration and trajectory correction signals for precise target localization.
  • Employing blockchain technology for secure, decentralized organization of video target tracking tasks.
  • Implementing adaptive clustering to guide small target localization across distributed nodes.
  • Applying result stabilization-based trajectory optimization to reduce inter-frame jitter.

Main Results:

  • The proposed feature location method outperforms existing techniques on CarChase2 and BSA datasets, showing significant improvements in recall and precision.
  • The video target tracking and correction model demonstrates superior performance compared to existing models, achieving high recall and precision on both datasets.
  • Experimental results validate the system's effectiveness in enhancing accuracy, robustness, and stability for video target tracking.

Conclusions:

  • The integrated system offers a comprehensive solution for video target tracking, addressing key challenges like occlusion and small target detection.
  • The synergy between feature location, blockchain, and trajectory optimization provides a robust and accurate tracking mechanism.
  • This approach holds significant promise for diverse video analytics applications, including surveillance, autonomous driving, and sports analysis.