mlsanityData to 7 Oct 2026

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

arXiv 2202.13121 ↗First posted 26 Feb 2022cs.CV cs.AIcited by 15

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Abstract (arXiv)

Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale & viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 142 of its 310 references that are in the map. First posted on arXiv 26 Feb 2022.

Builds on

142 of 310 references.

References · 142

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  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 784
  6. 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 685
  7. 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 547
  8. 2017Person Transfer GAN to Bridge Domain Gap for Person Re-Identificationcited by 479
  9. 2017Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)cited by 472
  10. 2021Rethinking Spatial Dimensions of Vision Transformerscited by 361
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  12. 2019Omni-Scale Feature Learning for Person Re-Identificationcited by 292
38 more
  1. 2020Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identificationcited by 214
  2. 2018Harmonious Attention Network for Person Re-Identificationcited by 211
  3. 2019Joint Discriminative and Generative Learning for Person Re-identificationcited by 211
  4. 2016Person Re-identification: Past, Present and Futurecited by 180
  5. 2017Beyond triplet loss: a deep quadruplet network for person re-identificationcited by 172
  6. 2017Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identificationcited by 168
  7. 2020Style Normalization and Restitution for Generalizable Person Re-identificationcited by 136
  8. 2018PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Modelcited by 133
  9. 2019Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identificationcited by 133
  10. 2019ABD-Net: Attentive but Diverse Person Re-Identificationcited by 126
  11. 2019Relation-Aware Global Attention for Person Re-identificationcited by 124
  12. 2021Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformercited by 119
  13. 2020Identity-Guided Human Semantic Parsing for Person Re-Identificationcited by 118
  14. 2020Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identificationcited by 110
  15. 2020High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identificationcited by 105
  16. 2018Human Semantic Parsing for Person Re-identificationcited by 100
  17. 2017Improving Person Re-identification by Attribute and Identity Learningcited by 95
  18. 2020Unsupervised Pre-training for Person Re-identificationcited by 94
  19. 2018Part-Aligned Bilinear Representations for Person Re-identificationcited by 90
  20. 2017Pose-driven Deep Convolutional Model for Person Re-identificationcited by 86
  21. 2020Long-Term Cloth-Changing Person Re-identificationcited by 85
  22. 2018Dissecting Person Re-identification from the Viewpoint of Viewpointcited by 83
  23. 2020Joint Disentangling and Adaptation for Cross-Domain Person Re-Identificationcited by 83
  24. 2020AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identificationcited by 81
  25. 2019Interaction-and-Aggregation Network for Person Re-identificationcited by 80
  26. 2016A Discriminatively Learned CNN Embedding for Person Re-identificationcited by 78
  27. 2020Meta Batch-Instance Normalization for Generalizable Person Re-Identificationcited by 77
  28. 2021Generalizable Person Re-identification with Relevance-aware Mixture of Expertscited by 77
  29. 2017A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Rankingcited by 74
  30. 2019Learning Generalisable Omni-Scale Representations for Person Re-Identificationcited by 73
  31. 2018Unsupervised Domain Adaptive Re-Identification: Theory and Practicecited by 69
  32. 2020Pose-guided Visible Part Matching for Occluded Person ReIDcited by 69
  33. 2017Deeply-Learned Part-Aligned Representations for Person Re-Identificationcited by 68
  34. 2019Mixed High-Order Attention Network for Person Re-Identificationcited by 65
  35. 2020Appearance-Preserving 3D Convolution for Video-based Person Re-identificationcited by 63
  36. 2018Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approachcited by 58
  37. 2018Densely Semantically Aligned Person Re-Identificationcited by 58
  38. 2021Group-aware Label Transfer for Domain Adaptive Person Re-identificationcited by 58

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 4; 2026: 2.

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  1. 2024Open-Set Biometrics: Beyond Good Closed-Set Modelscited by 12
  2. 2023Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learningcited by 8
  3. 2024DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDcited by 8
  4. 2025SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identificationcited by 7
  5. 2025Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Roomcited by 6
  6. 2024From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identificationcited by 5
  7. 2024DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-Worldcited by 3
  8. 2026LocalScore: Local Density-Aware Similarity Scoring for Biometricscited by 3
  9. 2024Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identificationcited by 2
  10. 2024Multi-Camera Industrial Open-Set Person Re-Identification and Trackingcited by 1
  11. 2025Person detection and re-identification in open-world settings of retail stores and public spacescited by 1
  12. 2024Multi-Camera Multi-Person Association using Transformer-Based Dense Pixel Correspondence Estimation and Detection-Based Maskingcited by 0
3 more
  1. 2024Improving analytical color and texture similarity estimation methods for dataset-agnostic person reidentificationcited by 0
  2. 2025Evolution of ReID: From Early Methods to LLM Integrationcited by 0
  3. 2026VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Visioncited by 0