MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations
Xuzhao Li, Xuchen Li, Shiyu Hu

TL;DR
MATrack is a novel multiscale adaptive tracking system designed for real-time nighttime UAV operations, addressing low-light and dynamic background challenges with three core modules, significantly improving accuracy and robustness.
Contribution
The paper introduces MATrack, a new multiscale adaptive tracking framework specifically optimized for nighttime UAV tracking, combining three innovative modules for enhanced performance.
Findings
Outperforms state-of-the-art methods on UAVDark135 benchmark by 5.9% in precision.
Maintains real-time processing at 81 FPS.
Validated on real UAV platform for reliable nighttime tracking.
Abstract
Nighttime UAV tracking faces significant challenges in real-world robotics operations. Low-light conditions not only limit visual perception capabilities, but cluttered backgrounds and frequent viewpoint changes also cause existing trackers to drift or fail during deployment. To address these difficulties, researchers have proposed solutions based on low-light enhancement and domain adaptation. However, these methods still have notable shortcomings in actual UAV systems: low-light enhancement often introduces visual artifacts, domain adaptation methods are computationally expensive and existing lightweight designs struggle to fully leverage dynamic object information. Based on an in-depth analysis of these key issues, we propose MATrack-a multiscale adaptive system designed specifically for nighttime UAV tracking. MATrack tackles the main technical challenges of nighttime tracking…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
