Reliability-Aware Semantic Gating with Retrieval-Augmented Association for Online Multi-Object Tracking Under...

Industrial low-altitude multi-object tracking is challenged by overhead viewpoints, dense small targets, mutual occlusion, homogeneous appearances, and domain-shifted backgrounds. Vision-language pre-trained models provide useful semantic priors, but their category-level representations can mislead instance-level association when many same-class targets are densely distributed. This study develops an OC-SORT-based framework with reliability-aware semantic gating and retrieval-augmented trajectory memory. On the MOT17 reporting split (MOT17-09/10/11/13) with FRCNN detections, the FullModel achieves 46.41% MOTA, 40.00% IDF1, 34.542% HOTA, 26.686% AssA, and 382 identity switches for seed 42. Relative to fixed-weight CLIP+FAISS, it reduces identity switches and fragmentations while trading 0.84 MOTA points for a 0.68-point IDF1 gain; exact reruns with three seeds reproduce the same MOTA, IDF1, and IDSw totals. The VisDrone-derived subset is used only as an industrial low-altitude proxy stress test: its high precision coexists with very low recall, so the result exposes domain-shift limitations, rather than demonstrating deployment-level industrial performance.