Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking

Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking

Abstract Unmanned aerial vehicle (UAV) tracking is a critical task in surveillance, security, and autonomous navigation applications. In this study, we propose graph neural network-tracker (GNN-tracker), a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-tempora...

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Bibliographic Details
Main Authors: Karim Dabbabi, Tijeni Delleji
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Unmanned aerial vehicle tracking
Graph neural network
Multi-sensor fusion
Transformer network
Real-time tracking
Spatiotemporal modelling
Online Access:https://doi.org/10.1186/s42492-025-00200-2
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https://doi.org/10.1186/s42492-025-00200-2

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