Occluded person re-identification (Re-ID) is a challenging task, as non-target pedestrians or surrounding obstacles often interfere with the visual cues of the target person, making it difficult for models to effectively learn discriminative feature representations. Most existing methods focus on salient body parts via spatial partitioning or external cues; however, they are either limited in capturing diverse semantic information or tend to introduce additional network complexity. To address these issues, we propose a Diversity Feature Learning Network (DFLNet). Specifically, a Scene-Level Occlusion (SLO) strategy is designed to automatically simulate two common occlusion scenarios by modeling the relative spatial relationships between the target person and surrounding occluders in real-world scenes. Subsequently, multiple class tokens are introduced to capture diverse representations of the target identity. A Token Diversity Constraint (TDC) loss is further imposed on these class tokens to encourage the learning of discriminative and diverse feature embeddings. Finally, we design a Diversity Feature Fusion (DFF) module, which facilitates the interaction and integration of dual-branch features by modeling global feature correlations and optimizing inter-feature distribution distances. Extensive experiments on occluded, partial, and holistic Re-ID datasets demonstrate the effectiveness of the proposed DFLNet.

Unmanned aerial vehicle (UAV) multi-object tracking (MOT) is challenging because small targets produce unstable confidence and localization, while viewpoint changes and short occlusions make association ambiguous. We present OATrack, an integrated association framework tailored to UAV small-object tracking. OATrack combines detection–track pair quality based on detection reliability and current-state geometric consistency, quality-gated track-state protection with a track-specific scale prototype, and three-stage progressive assignment for active-track matching and recently lost-track recovery. A standardized YOLO11m-smallobj detection cache supplies identical detections to all association methods and enables reproducible fixed-input evaluation. On VisDrone-MOT val, OATrack reaches HOTA 38.5847 versus 38.4797 for official Ultralytics ByteTrack while reducing identity switches (IDSW) by 69.2%, track fragmentation by 29.7%, and false positives by 68.8%. On the complete 20-sequence UAVDT test split, OATrack improves HOTA, MOTA, and IDF1 from 41.4345, 7.5162, and 51.4916 to 42.7743, 24.8760, and 53.2829, respectively, and reduces IDSW from 639 to 295. HOTA-component and size/occlusion analyses further demonstrate stronger association consistency and false-positive control across the two UAV benchmarks.