Temporal Geometry-Aware Local Search for Thermal Infrared Anti-UAV Tracking

Thermal infrared (TIR) UAV tracking is challenged by small targets, weak appearance cues, and large inter-frame displacement. For bounded local trackers that restrict the search to a crop around the previous target estimate, once the target leaves the search crop, appearance modeling alone cannot recover the missing visual evidence. Expanding the search region may introduce more background clutter, while ambiguous local cues make it difficult to decide where to search. We therefore view bounded recovery as two distinct decisions: where to acquire additional evidence and which hypothesis to trust. Based on this view, we propose TGTrack, which decouples search allocation from visual selection. A time-aware Temporal Geometry Branch models sparsely sampled trajectory states in global coordinates by encoding center and scale evolution, visual confidence, and temporal information, and predicts target geometry and localization reliability. When the predicted center leaves the normal search support, TGTrack allocates one bounded geometry-guided Recrop. We further introduce Gate–Hann, which modulates the within-Recrop spatial prior according to predicted reliability. Final cross-region selection remains appearance-driven using raw visual confidence. Extensive experiments show that TGTrack achieves state-of-the-art results among the compared local-search trackers, improves the controlled LoRAT-L baseline by 2.86 AUC points on AntiUAV410 while running at 292.01 FPS, and by 4.03 points on AntiUAV under direct transfer without target-domain adaptation. Code will be released upon acceptance.