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Multi-input and Multi-variable systems01:22

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Toward based on concentrated multi-scale linear attention real-time UAV tracking using joint natural language

Guocai Du1, Peiyong Zhou1, Nurbiya Yadikar1

  • 1School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China.

Scientific Reports
|April 26, 2025
PubMed
Summary

This study introduces a unified framework for unmanned aerial vehicle (UAV) tracking using natural language descriptions. The novel approach integrates visual grounding and object tracking, improving performance and enabling end-to-end training for more accurate UAV missions.

Keywords:
Concentrated multi-scale linear attentionNatural language specificationTriangular integrationUnmanned aerial vehicle tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Unmanned aerial vehicle (UAV) tracking traditionally uses separate visual grounding and object tracking steps.
  • This separation overlooks the semantic information from natural language and prevents end-to-end training.

Purpose of the Study:

  • To develop an integrated framework for UAV tracking that unifies visual grounding and object tracking using natural language specifications.
  • To enable end-to-end training and leverage semantic information for improved tracking accuracy.

Main Methods:

  • A novel framework integrating visual grounding and object tracking as a unified task based on natural language.
  • Triangular integration to establish relationships between natural language and images (template and search).
  • A lightweight concentrated multi-scale linear attention mechanism with residuals for efficiency and multi-scale learning.

Main Results:

  • The proposed tracker achieved high performance across six UAV tracking datasets.
  • Key metrics include an accuracy of 0.819, a success rate of 0.654, and an average speed of 61 FPS.
  • Outperformed existing state-of-the-art trackers in UAV visual tracking.

Conclusions:

  • The integrated framework effectively unifies visual grounding and object tracking for natural language-specified UAV missions.
  • The novel attention mechanism and residual integration contribute to improved tracking accuracy and efficiency.
  • This approach offers a more robust and adaptable solution for UAV target tracking.