Person reidentification (ReID) technology is considered to perform relatively well under controlled, ground-level conditions, but also to break down when deployed in challenging real-world settings. This is due to extreme data variability factors such as resolution, viewpoint changes, scale variations, occlusions, and appearance shifts from clothing/session drifts. Also, the publicly available data sets do not realistically incorporate such kinds and magnitudes of variability, which limits the progress of this technology. This paper introduces DetReIDX, a large-scale aerial-ground person dataset, that was explicitly designed as a stress test to ReID under real-world conditions. DetReIDX is a multi-session set that includes over 18 million bounding boxes from 553 identities, collected in seven university campuses from three continents, with drone altitudes between 5.8 and 120 meters. Singularly, as a key novelty, DetReIDX subjects were recorded in (at least) two sessions on different days, with changes in clothing, daylight and location, making it suitable to actually evaluate long-term person ReID. Further, data were annotated from 16 soft biometric attributes and multitask labels for detection, tracking, ReID, and action recognition. In order to provide empirical objective evidence of DetReIDX usefulness, we considered the specific tasks of human detection, ReID and tracking, and observed that SOTA methods catastrophically degrade performance (up to 80% in detection accuracy and over 70% in Rank-1 ReID) when exposed to DetReIDX’s conditions. The dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/

Video-based person re-identification (re-identification) remains underused in real-world deployments, despite impressive benchmark performance. Most existing models rely on superficial correlations—such as clothing, background, or lighting—that fail to generalize across domains, viewpoints, and temporal variations. This study examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based re-identification. We provide a structured and critical analysis of methods that leverage structural causal models (SCMs), interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. This study is organized around a novel taxonomy of causal re-identification methods, spanning generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess the practical challenges—scalability, fairness, interpretability, and privacy—that must be addressed for real-world adoption. Finally, we identify open problems and outline future research directions that integrate causal modeling with efficient architectures and self-supervised learning. This study aims to establish a coherent foundation for causal video-based person re-identification and catalyze the next phase of research in this rapidly evolving domain.

Reliable fabric inspection is difficult to automate because production-line imagery differs substantially from the controlled samples used by many public benchmarks. This paper introduces LUSITANOv2, a high-resolution fabric-defect dataset collected at an active textile inspection station with an industrial line-scan camera and directional illumination. It contains 25,120 native images, including both defect-containing and defect-free fabric, together with 18,557 class-agnostic bounding boxes. We establish supervised baselines with YOLOv12n, Faster R-CNN, and RT-DETR-L and evaluate nine one-class anomaly-detection methods. RT-DETR-L achieves the highest mAP@0.5 in the matched detector comparison, reaching 0.610, compared with 0.597 for Faster R-CNN and 0.592 for YOLOv12n. In a separate controlled YOLOv12n resolution study, retaining more native weave detail improves detection performance, reaching 0.598 mAP@0.5 at the highest tested resolution. A post hoc analysis finds no simple recall penalty for defects that touch the image boundary. Matched zero-shot experiments across LUSITANOv2, TILDA, and ZJU-Leaper reveal substantial cross-dataset degradation despite stronger in-domain results. LUSITANOv2 provides a realistic benchmark for localized defect detection, one-class anomaly detection, resolution-sensitive line-scan processing, and future work on transfer to active textile-production settings.

Video-based person re-identification (ReID) in cross-view domains (for example, aerial-ground surveillance) remains an open problem because of extreme viewpoint shifts, scale disparities, and temporal inconsistencies. To address these challenges, we propose MTF-CVReID, a parameter-efficient framework that introduces seven complementary modules over a ViT-B/16 backbone. Specifically, we include: (1) Cross-Stream Feature Normalization (CSFN) to correct camera and view biases; (2) Multi-Resolution Feature Harmonization (MRFH) for scale stabilization across altitudes; (3) Identity-Aware Memory Module (IAMM) to reinforce persistent identity traits; (4) Temporal Dynamics Modeling (TDM) for motion-aware short-term temporal encoding; (5) Inter-View Feature Alignment (IVFA) for perspective-invariant representation alignment; (6) Hierarchical Temporal Pattern Learning (HTPL) to capture multi-scale temporal regularities; and (7) Multi-View Identity Consistency Learning (MVICL) that enforces cross-view identity coherence using a contrastive learning paradigm. Despite adding only about 2 million parameters and 0.7 GFLOPs over the baseline, MTF-CVReID maintains real-time efficiency (189 FPS) and achieves state-of-the-art performance on the AG-VPReID benchmark across all altitude levels, with strong cross-dataset generalization to G2A-VReID and MARS datasets. These results show that carefully designed adapter-based modules can substantially enhance cross-view robustness and temporal consistency without compromising computational efficiency. The source code is available at https://github.com/MdRashidunnabi/MTF-CVReID