UAV RGB-NIR Tracking via Quality-Gated Dynamic Template Update in OSTrack
Year: 2026
Type: article
Abstract: Existing single-modality object-tracking methods for unmanned aerial vehicles (UAVs) suffer from template mismatch under illumination variation, target occlusion, and fast motion, leading to tracking failure. We therefore propose an algorithm based on the one-stream Transformer tracking framework OSTrack. Our method retains the joint feature learning and candidate target pre-elimination mechanisms while introducing dynamic template updating and quality scoring judgment modules. Dynamic template updating balances the strengths of static and dynamic templates by utilizing both initial and historical target state information. This approach prevents tracking drift from target state changes and improves target appearance. Additionally, a gating strategy based on quality evaluation screens candidate update templates to suppress contamination caused by occlusion, modal mutation, and false detections. Comprehensive experiments on the public CMOTB dataset and our self-built UAV RGB-NIR dataset validated the method’s effectiveness. For CMOTB, a 68.7% success rate (SR) and 66.5% precision (P) was achieved, outperforming vanilla OSTrack by 6.4 and 5.8 percentage points, respectively. For UAV RGB-NIR, it yielded a 74.6% SR and 76.8% P, surpassing the baseline OSTrack under complex real scenarios. The proposed method enhanced the robustness and adaptability of UAV RGB-NIR object tracking while maintaining favorable real-time performance, indicating a strong potential for practical application.
Source: Drones
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7214068461