MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations

2025-10-24

AI Summary

Nighttime UAV tracking suffers from severe drift under low-light conditions, cluttered backgrounds, and large viewpoint variations; existing methods are hindered by enhancement artifacts, high computational overhead for domain adaptation, and inadequate dynamic target modeling. This paper proposes a multi-scale adaptive tracking system comprising a Multi-scale Hierarchical Blending (MHB) module, an Adaptive Key-Token Gating mechanism, and a Nighttime Template Calibrator (NTC), jointly enhancing feature consistency, background discriminability, and template robustness. Evaluated on the UAVDark135 dataset, our method achieves new state-of-the-art performance—improving precision, normalized precision, and AUC by 5.9%, 5.4%, and 4.2%, respectively—while operating at 81 FPS. Real-world deployment on an embedded UAV platform validates its real-time capability and operational reliability for mission-critical applications such as search-and-rescue and border surveillance.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Tracking, profiling, and countermeasures against them

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 through the collaborative work of three core modules: Multiscale Hierarchy Blende (MHB) enhances feature consistency between static and dynamic templates. Adaptive Key Token Gate accurately identifies object information within complex backgrounds. Nighttime Template Calibrator (NTC) ensures stable tracking performance over long sequences. Extensive experiments show that MATrack achieves a significant performance improvement. On the UAVDark135 benchmark, its precision, normalized precision and AUC surpass state-of-the-art (SOTA) methods by 5.9%, 5.4% and 4.2% respectively, while maintaining a real-time processing speed of 81 FPS. Further tests on a real-world UAV platform validate the system's reliability, demonstrating that MATrack can provide stable and effective nighttime UAV tracking support for critical robotics applications such as nighttime search and rescue and border patrol.

Problem

• Addresses nighttime UAV tracking challenges in low-light conditions
• Solves tracker drift from cluttered backgrounds and viewpoint changes
• Overcomes computational limitations of existing domain adaptation methods

Innovation

• Multiscale Hierarchy Blende enhances static and dynamic feature consistency
• Adaptive Key Token Gate identifies objects in complex backgrounds
• Nighttime Template Calibrator ensures stable long-sequence tracking performance