Skip to main content
SpringerLink
Log in

Camera-aware progressive learning for unsupervised person re-identification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Unsupervised person re-identification (Re-ID) is a challenging task due to the problems of the view-specific gap across different cameras and the missing of ground truth labels. The intra-class variance caused by the camera domain gap makes the model difficult to distinguish cross-view discriminative information. The noisy pseudo labels may be misguide the model toward deteriorated over-fitting. Improving the clustering accuracy of pseudo labels and alleviating the intra-class variance can improve the performance of unsupervised person Re-ID. Most methods improve clustering accuracy by optimizing the clustering distribution of instance features in the memory bank. However, the optimized distribution is still unbalanced and has a large intra-class gap. In order to address this problem, we propose a camera-aware progressive learning (CAPL) for unsupervised person re-identification. In CAPL, on the one hand, clustering edge features are explored and used to update all instance features belonging to the same pseudo label in the memory bank, which makes the instance feature distribution more balanced; on the other hand, camera-relevant features are explored to update all instance features with the same pseudo-labels in the memory bank as well as the corresponding camera class centroids, which makes the model effectively reduces the intra-class variance. This process is performed iteratively, and finally, the global clustering distribution can be achieved progressively. Extensive experiments on four person Re-ID benchmarks and a vehicle Re-ID benchmark demonstrate that our proposed approach outperforms the state-of-the-art methods in terms of mAP and CMC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Deng W, Zheng L, Ye Q, et al (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: CVPR, pp 994–1003

  2. Yang F, Zhong Z, Luo Z et al (2020) Leveraging virtual and real person for unsupervised person re-identification. IEEE Trans Multimedia 22(9):2444–2453

    Article  Google Scholar 

  3. Ning X, Gong K, Li W et al (2021) Feature refinement and filter network for person re-identification. IEEE Trans Circuits Syst Video Technol 31(9):3391–3402

    Article  Google Scholar 

  4. Yan C, Pang G, Bai X et al (2022) Beyond triplet loss: person re-identification with fine-grained difference-aware pairwise loss. IEEE Trans Multimed 24:1665–1677

    Article  Google Scholar 

  5. Lin Y, Dong X, Zheng L, et al (2019) A bottom-up clustering approach to unsupervised person re-identification. In: AAAI, pp 8738–8745

  6. Wang D, Zhang S (2020) Unsupervised person re-identification via multi-label classification. In: CVPR, pp 10,978–10,987

  7. Zeng K, Ning M, Wang Y, et al (2020) Hierarchical clustering with hard-batch triplet loss for person re-identification. In: CVPR, pp 13,654–13,662

  8. Li YJ, Lin CS, Lin YB, et al (2019) Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: ICCV, pp 7918–7928

  9. Ji Z, Zou X, Lin X, et al (2020) An attention-driven two-stage clustering method for unsupervised person re-identification. In: ECCV, pp 20–36

  10. Fu Y, Wei Y, Wang G, et al (2019) Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: ICCV, pp 6111–6120

  11. Wei L, Zhang S, Gao W, et al (2018) Person transfer gan to bridge domain gap for person re-identification. In: CVPR, pp 79–88

  12. Li D, Li D, Zhang Z, et al (2019) Unsupervised cross-domain person re-identification: a new framework. In: ICIP, pp 1222–1226

  13. Zhou S, Wang Y, Zhang F et al (2021) Cross-view similarity exploration for unsupervised cross-domain person re-identification. Neural Comput Appl 9(33):4001–4011

    Article  Google Scholar 

  14. Zhang W, Huang L, Wei Z et al (2021) Angular regularization for unsupervised domain adaption on person re-identification. Neural Comput Appl 24(33):17041–17056

    Article  Google Scholar 

  15. Yang F, Zhong Z, Luo Z, et al (2021) Joint noise-tolerant learning and meta camera shift adaptation for unsupervised person re-identification. In: CVPR, pp 4853–4862

  16. Xuan S, Zhang S (2021) Intra-inter camera similarity for unsupervised person re-identification. In: CVPR, pp 11,921–11,930

  17. Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: CVPR, pp 1735–1742

  18. Wu Z, Xiong Y, Yu SX, et al (2018) Unsupervised feature learning via non-parametric instance discrimination. In: CVPR, pp 3733–3742

  19. Ge Y, Zhu F, Chen D, et al (2020) Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. In: NeurIPS, pp 11,309–11,321

  20. Shu X, Yuan D, Liu Q et al (2020) Adaptive weight part-based convolutional network for person re-identification. Multimed Tools Appl 79(31):23617–23632

    Article  Google Scholar 

  21. Zhong Z, Zheng L, Luo Z, et al (2019) Invariance matters: exemplar memory for domain adaptive person re-identification. In: CVPR, pp 598–607

  22. Yu HX, Zheng WS (2020) Weakly supervised discriminative feature learning with state information for person identification. In: CVPR, pp 5527–5537

  23. Qi L, Wang L, Huo J, et al (2019) A novel unsupervised camera-aware domain adaptation framework for person re-identification. In: ICCV, pp 8079–8088

  24. Zhang X, Cao J, Shen C, et al (2019) Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: ICCV, pp 8221–8230

  25. Lin Y, Xie L, Wu Y, et al (2020) Unsupervised person re-identification via softened similarity learning. In: CVPR, pp 3387–3396

  26. Yin Q, Wang G, Ding G et al (2021) Multi-view label prediction for unsupervised learning person re-identification. IEEE Signal Process Lett 28:1390–1394

    Article  Google Scholar 

  27. Lu J, He Y, Liu T et al (2019) Centralized and clustered features for person re-identification. IEEE Signal Process Lett 26(6):933–937

    Article  Google Scholar 

  28. Zhong Z, Zheng L, Li S, et al (2018) Generalizing a person retrieval model hetero- and homogeneously. In: ECCV, pp 176–192

  29. Yang Q, Yu HX, Wu A, et al (2019) Patch-based discriminative feature learning for unsupervised person re-identification. In: CVPR, pp 3628–3637

  30. Jin X, Lan C, Zeng W, et al (2020) Global distance-distributions separation for unsupervised person re-identification. In: ECCV, pp 735–751

  31. Wang M, Lai B, Huang J, et al (2021) Camera-aware proxies for unsupervised person re-identification. In: AAAI

  32. He K, Fan H, Wu Y, et al (2020) Momentum contrast for unsupervised visual representation learning. In: CVPR, pp 9726–9735

  33. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778

  34. Ester M, Kriegel HP, Sander J, et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp 226–231

  35. Zheng L, Shen L, Tian L, et al (2015) Scalable person re-identification: a benchmark. In: ICCV, pp 1116–1124

  36. Ristani E, Solera F, Zou R, et al (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV, pp 17–35

  37. Sun X, Zheng L (2019) Dissecting person re-identification from the viewpoint of viewpoint. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 608–617

  38. Liu X, Liu W, Mei T, et al (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: ECCV, pp 869–884

  39. Liu X, Liu W, Mei T et al (2018) Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20(3):645–658

    Article  Google Scholar 

  40. Diederik PK, Jimmy B (2015) Adam: a method for stochastic optimization. In: ICLR

  41. Zhong Z, Zheng L, Cao D, et al (2017) Re-ranking person re-identification with k-reciprocal encoding. In: CVPR, pp 3652–3661

  42. Li J, Zhang S (2020) Joint visual and temporal consistency for unsupervised domain adaptive person re-identification. In: ECCV, pp 483–499

  43. Ding Y, Fan H, Xu M et al (2020) Adaptive exploration for unsupervised person re-identification. ACM Trans Multimed Comput Commun Appl 16(1):1–19

    Article  Google Scholar 

  44. Li M, Sun H, Lin C et al (2022) The devil in the tail: cluster consolidation plus cluster adaptive balancing loss for unsupervised person re-identification. Pattern Recogn 129(108):763

    Google Scholar 

  45. Yin J, Zhang S, Xie J et al (2022) Unsupervised person re-identification via simultaneous clustering and mask prediction. Pattern Recogn 126(108):568

    Google Scholar 

  46. van der Maaten L, Hinton G (2008) Viualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61976034,U1808206), the Dalian Science and Technology Innovation Fund (2022JJ12GX013), the Liaoning Natural Science Foundation(2022-YGJC-20), and the Fundamental Research Funds for the Central Universities (DUT21YG106).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116023, China

    Yuxuan Liu, Hongwei Ge, Liang Sun & Yaqing Hou

Authors
  1. Yuxuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaqing Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Ge.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Ge, H., Sun, L. et al. Camera-aware progressive learning for unsupervised person re-identification. Neural Comput & Applic 35, 11359–11371 (2023). https://doi.org/10.1007/s00521-023-08301-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08301-w

Keywords

Search

Navigation