Occluded person re-identification: a taxonomic survey and reproducible empirical benchmark

Research and Performance Analysis of Person Re-Identification Methods in Occluded Scenarios

It is shown that feature-level enhancement methods achieve outstanding performance in occluded scenarios and good generalization in non-occluded scenarios, yielding the best overall effectiveness.

Occlusion-Aware Cross-Modality Completion Network for Occluded Visible-Infrared Person Re-Identification

Robust video person re-identification via hierarchical cross-attention mechanisms

Exploring identity misalignment in unsupervised visible-infrared person re-identification

A novel Robust Modality Unified Learning framework (RMUL), consisting of a Robust Cross-modality Label Transfer (RCLT) method and Modality Unified Learning (MUL) module, which unifies cross-modality labels only for modality-shared instances while retaining the original labels for modality-specific ones.

A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification

A generative AI integrated multimodal ReID framework designed explicitly for robustness under missing cues and low latency deployment is proposed, with a cost aware early-exit cascade that prioritizes inexpensive, high confidence evidence and only triggers expensive modalities for ambiguous cases.

PERSON RE-IDENTIFICATION BASED ON DEEP LEARNING NETWORKS: A SURVEY

This survey offers an updated and focused review of deep learning-based ReID methods, encompassing research from 2020 to 2025, and investigates in-depth the engineering aspects, including system integration, real-time performance, and sensor constraints, which are often overlooked in reviews of earlier work.