Rethinking Occlusion Modeling for UAV Tracking

Occlusion remains one of the major challenges in UAV tracking, where dynamic viewpoints and complex environments often cause partial or complete visibility loss.Existing transformer-based trackers typically regard occlusion as random information dropout, overlooking its structured and spatially correlated nature in real-world scenes.We rethink occlusion modeling in UAV tracking as a structured process governed by spatial dependencies.Based on this insight, we introduce Clustered Occlusion Modeling (COM) to generate realistic, density-adaptive occlusion patterns that enhance feature robustness under partial visibility.Furthermore, we design Cost-Aware Depth Bias (CADB), which employs a depth-dependent prior to adjust inference depth, yielding better efficiency while maintaining competitive accuracy.Integrating COM and CADB into a unified single-stream transformer framework, termed OCTrack, our tracker achieves robust and efficient UAV tracking in occlusion-prone environments.Extensive experiments on multiple UAV benchmarks validate its effectiveness and demonstrate state-of-the-art performance.