OCTA-SOT: Online Cross-Modal Trajectory Adjustment for RGBT Anti-UAV Single Object Tracking under Spatio-Temporal Misalignment

Due to the extensive application of UAVs in modern war-fare, anti-UAV single object tracking has garnered increasing researchinterest. However, prevailing multi-modal methods heavily rely on theassumption of perfectly calibrated sensors, leaving the critical challengeof tracking under realistic spatio-temporal misalignment largely unad-dressed. To tackle this issue, we formally introduce the novel task ofuncalibrated RGBT anti-UAV tracking and mathematically formulateits observation process as a unified state-space model. Based on thistheoretical foundation, we propose OCTA-SOT, an Online Cross-modalTrajectory Adjustment Single Object Tracking framework. As a com-pletely training-free and plug-and-play module, it can be directly appliedto off-the-shelf base trackers. During inference, it adaptively selects reli-able trajectory information to update inter-modality mappings in realtime, while simultaneously calibrating unconfident trackers and theirresults. At its core, a customized Kalman-driven mechanism dynami-cally adjusts these mappings, thereby achieving robust target tracking.Extensive experiments on the challenging Anti-UAV300 dataset demon-strate the exceptional effectiveness of our method. Without requiring anytraining, OCTA-SOT effectively enhances the DIMP tracker, achievingabsolute improvements of 9.3% in AUC, 12.9% in Precision, and 11.9%in Normalized Precision, while simultaneously outperforming a series ofcurrent state-of-the-art methods. The source code is publicly availableat https://github.com/xkliu-eps/AntiUAVRGBTTracking/.