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Space dynamic target tracking method based on five-frame difference and Deepsort.

Cheng Huang1, Quanli Zeng2, Fangyu Xiong2

  • 1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, China. huangchengsunxi@163.com.

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|March 13, 2024
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Summary
This summary is machine-generated.

This study introduces an improved online tracking method combining five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric) for space dynamic target tracking. The enhanced algorithm achieves 93.88% tracking precision, effectively handling occlusion and background interference.

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Area of Science:

  • Computer Vision
  • Robotics
  • Aerospace Engineering

Background:

  • Space dynamic target tracking is challenged by occlusion and background interference.
  • Existing methods struggle with real-time performance and accuracy in complex environments.
  • Robust identification and tracking are crucial for space missions.

Purpose of the Study:

  • To develop an enhanced online tracking method for space dynamic targets.
  • To improve tracking accuracy and robustness against occlusion and interference.
  • To achieve real-time identification and tracking of dynamic targets.

Main Methods:

  • An improved five-frame difference method integrated with Visual Background Extraction (ViBe) for enhanced accuracy and anti-interference.
  • An enhanced YOLOv5s (You Look Only Once) detector using Discrete Wavelet Transformation (DWT) preprocessing and Global Attention Module (GAM) injection.
  • Integration of the enhanced YOLOv5s detector with Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric) for robust tracking, especially during occlusion.

Main Results:

  • The proposed method achieves a tracking precision of 93.88% in simulations.
  • Stable tracking of dynamic targets was demonstrated under background interference and occlusion.
  • Experiments with a D435i depth camera confirmed effectiveness in strong light and occlusion scenarios.

Conclusions:

  • The combined five-frame difference and Deepsort method significantly enhances space dynamic target tracking.
  • The improved YOLOv5s detector and ViBe integration contribute to superior accuracy and real-time performance.
  • The algorithm shows effectiveness and superiority for recognition and tracking in challenging space environments.