mlsanityData to 7 Oct 2026

Deep Learning-based Occluded Person Re-identification: A Survey

arXiv 2207.14452 ↗First posted 29 Jul 2022cs.CVcited by 12

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Abstract (arXiv)

Occluded person re-identification (Re-ID) aims at addressing the occlusion problem when retrieving the person of interest across multiple cameras. With the promotion of deep learning technology and the increasing demand for intelligent video surveillance, the frequent occlusion in real-world applications has made occluded person Re-ID draw considerable interest from researchers. A large number of occluded person Re-ID methods have been proposed while there are few surveys that focus on occlusion. To fill this gap and help boost future research, this paper provides a systematic survey of occluded person Re-ID. Through an in-depth analysis of the occlusion in person Re-ID, most existing methods are found to only consider part of the problems brought by occlusion. Therefore, we review occlusion-related person Re-ID methods from the perspective of issues and solutions. We summarize four issues caused by occlusion in person Re-ID, i.e., position misalignment, scale misalignment, noisy information, and missing information. The occlusion-related methods addressing different issues are then categorized and introduced accordingly. After that, we summarize and compare the performance of recent occluded person Re-ID methods on four popular datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. Finally, we provide insights on promising future research directions.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 66 of its 129 references that are in the map. First posted on arXiv 29 Jul 2022.

Builds on

66 of 129 references.

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38 more
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50 most cited of 66 All 66 (JSON)

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 3.

12 papers.

  1. 2022Body Part-Based Representation Learning for Occluded Person Re-Identificationcited by 63
  2. 2023Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identificationcited by 19
  3. 2024Keypoint Promptable Re-Identificationcited by 15
  4. 2023Occluded Person Re-Identification with Deep Learning: A Survey and Perspectivescited by 13
  5. 2023GraFT: Gradual Fusion Transformer for Multimodal Re-Identificationcited by 7
  6. 2023PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoderscited by 6
  7. 2024Part-Attention Based Model Make Occluded Person Re-Identification Strongercited by 5
  8. 2024From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identificationcited by 5
  9. 2024Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identificationcited by 3
  10. 2025SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuningcited by 2
  11. 2025MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillationcited by 1
  12. 2025Evolution of ReID: From Early Methods to LLM Integrationcited by 0