Abstract
Single-object tracking from unmanned aerial vehicles (UAVs) is complicated by small target size, rapid camera ego-motion, and frequent occlusion, all of which degrade the appearance cues that Transformer trackers rely on. We present HOMA-ST, which recovers a complementary motion signal by decomposing the short-term dynamics of the bounding box into three physically meaningful branches — translation, geometric variation, and trajectory — and extracting a 21-dimensional high-order motion descriptor in which every dimension has an explicit interpretation and a known minimum-frame requirement. The three branches are fused by a branch-token self-attention block, and the resulting motion energy is injected as a learnable, gated residual on the search-side queries at every cross-attention layer. An auxiliary motion-consistency loss further couples spatial accuracy to temporal plausibility. On UAV123, UAVDT, DTB70, and VisDrone-SOT, HOMA-ST improves success AUC over OSTrack by 1.7 to 2.1%, with the largest gains on fast-motion and occlusion subsets. A further evaluation on 20 live-flight sequences recorded on a DJI Tello EDU platform confirms a 4.5% AUC improvement under real deployment conditions. The results indicate that HOMA-ST provides an effective framework for robust UAV tracking in both benchmark and real-flight scenarios.