Abstract
The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us to improve tracking accuracy, particularly in scenarios involving occluded UAVs or those with weak appearance features. By integrating the enhanced UAV detector with the formation-aware swarm tracking framework, our approach achieves notable advancements in both detection and tracking performance.
Recommended Citation
M. H. Rahman and S. Madria, "V-USDT: Vision-Based UAV Swarm Detection and Tracking by Leveraging Swarm Formation Constraints," Proceedings IEEE International Conference on Mobile Data Management, pp. 55 - 61, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/MDM65600.2025.00026
Department(s)
Computer Science
Keywords and Phrases
Computer Vision; Object Detection; Tracking; Unmanned Aerial Vehicle
International Standard Serial Number (ISSN)
1551-6245
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Comments
Army Research Office, Grant None