Skip to main content
SpringerLink
Log in

Ped-Mix: Mix Pedestrians for Occluded Person Re-identification

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14436))

Included in the following conference series:

Abstract

Occluded person re-identification is a very challenging task due to the interference of occluding objects. Most existing approaches concentrate on modifying the network architecture to facilitate the extraction of more distinctive local features or render the network less sensitive to occlusions. However, it is easy to fail when encountering previously unseen occlusions or when other humans act as occluders, due to the limited occlusion variance in the training set. In this paper, we propose a data augmentation method that blends the target pedestrian with other pedestrians to simulate non-target pedestrian occlusion. Furthermore, we propose a non-target suppression (NTS) loss to reduce the information flow from the occluded region to the final embedding, where the occluded region can be easily obtained from the augmentation. Experimental results demonstrate that this simple augmentation technique yields significant performance improvements in the task of occluded person re-identification.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abnar, S., Zuidema, W.: Quantifying attention flow in transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, January 2020. https://doi.org/10.18653/v1/2020.acl-main.385

  2. Cheng, X., Jia, M., Wang, Q., Zhang, J.: More is better: multi-source dynamic parsing attention for occluded person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6840–6849 (2022)

    Google Scholar 

  3. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  4. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  5. DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)

  6. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arxiv 2020. arXiv preprint arXiv:2010.11929 (2010)

  7. Gao, S., Wang, J., Lu, H., Liu, Z.: Pose-guided visible part matching for occluded person ReID. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11744–11752 (2020)

    Google Scholar 

  8. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proc. IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3, pp. 1–7 (2007)

    Google Scholar 

  9. He, K., Chen, X., Xie, S., Li, Y., Dollar, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. https://doi.org/10.1109/cvpr52688.2022.01553

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  11. He, L., Liang, J., Li, H., Sun, Z.: Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7073–7082 (2018)

    Google Scholar 

  12. He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: TransReID: transformer-based object re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15013–15022 (2021)

    Google Scholar 

  13. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  14. Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Feature completion for occluded person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4894–4912 (2021)

    Google Scholar 

  15. Huang, H., Li, D., Zhang, Z., Chen, X., Huang, K.: Adversarially occluded samples for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5098–5107 (2018)

    Google Scholar 

  16. Jia, M., Cheng, X., Lu, S., Zhang, J.: Learning disentangled representation implicitly via transformer for occluded person re-identification. IEEE Trans. Multimedia (2022)

    Google Scholar 

  17. Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2907 (2021)

    Google Scholar 

  18. Miao, J., Wu, Y., Liu, P., Ding, Y., Yang, Y.: Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 542–551 (2019)

    Google Scholar 

  19. Somers, V., De Vleeschouwer, C., Alahi, A.: Body part-based representation learning for occluded person re-identification. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1613–1623 (2023)

    Google Scholar 

  20. Tan, L., Dai, P., Ji, R., Wu, Y.: Dynamic prototype mask for occluded person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 531–540 (2022)

    Google Scholar 

  21. Wang, G., et al.: High-order information matters: learning relation and topology for occluded person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6449–6458 (2020)

    Google Scholar 

  22. Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349–3364 (2020)

    Article  Google Scholar 

  23. Wang, T., Liu, H., Song, P., Guo, T., Shi, W.: Pose-guided feature disentangling for occluded person re-identification based on transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2540–2549 (2022)

    Google Scholar 

  24. Wang, Z., Zhu, F., Tang, S., Zhao, R., He, L., Song, J.: Feature erasing and diffusion network for occluded person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4754–4763 (2022)

    Google Scholar 

  25. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)

    Google Scholar 

  26. Xia, J., Tan, L., Dai, P., Zhao, M., Wu, Y., Ji, R.: Attention disturbance and dual-path constraint network for occluded person re-identification. arXiv preprint arXiv:2303.10976 (2023)

  27. Ye, Y., Zhou, H., Yu, J., Hu, Q., Yang, W.: Dynamic feature pruning and consolidation for occluded person re-identification. arXiv preprint arXiv:2211.14742 (2022)

  28. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)

    Google Scholar 

  29. Zhao, C., Lv, X., Dou, S., Zhang, S., Wu, J., Wang, L.: Incremental generative occlusion adversarial suppression network for person ReID. IEEE Trans. Image Process. 30, 4212–4224 (2021)

    Article  Google Scholar 

  30. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

    Google Scholar 

  31. Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754–3762 (2017)

    Google Scholar 

  32. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001–13008 (2020)

    Google Scholar 

  33. Zhu, K., Guo, H., Liu, Z., Tang, M., Wang, J.: Identity-guided human semantic parsing for person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part III. LNCS, vol. 12348, pp. 346–363. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_21

    Chapter  Google Scholar 

  34. Zhuo, J., Chen, Z., Lai, J., Wang, G.: Occluded person re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Shang Gao, Chenyang Yu, Pingping Zhang & Huchuan Lu

  2. NingBo Institute of Dalian University of Technology, Ningbo, China

    Shang Gao & Huchuan Lu

Authors
  1. Shang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenyang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pingping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huchuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huchuan Lu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 207 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, S., Yu, C., Zhang, P., Lu, H. (2024). Ped-Mix: Mix Pedestrians for Occluded Person Re-identification. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14436. Springer, Singapore. https://doi.org/10.1007/978-981-99-8555-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8555-5_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8554-8

  • Online ISBN: 978-981-99-8555-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation