[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Multi-level feature learning with attention for person re-identification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Person re-identification (re-ID) aims to match a specific person in a large gallery with different cameras and locations. Previous part-based methods mainly focus on part-level features with uniform partition, which increases learning ability for discriminative feature but not efficient or robust to scenarios with large variances. To address this problem, in this paper, we propose a novel feature fusion strategy based on traditional convolutional neural network. Then, a multi-branch deeper feature fusion network architecture is designed to perform discriminative learning for three semantically aligned region. Based on it, a novel self-attention mechanism is employed to softly assign corresponding weights to the semantic aligned feature during back-propagation. Comprehensive experiments have been conducted on several large-scale benchmark datasets, which demonstrates that proposed approach yields consistent and competitive re-ID accuracy compared with current single-domain re-ID methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Chang X, Hospedales TM, Xiang T (2018) Multi-level factorisation net for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2109–2118

  2. Chen LC, Yang Y, Wang J, Xu W, Yuille AL (2016) Attention to scale: Scale-aware semantic image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3640–3649

  3. Chen Y, Zhu X, Gong S (2017) Person re-identification by deep learning multi-scale representations. In: IEEE International Conference on Computer Vision Workshops, pp 2590–2600

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

  5. Fang HS, Xie S, Tai YW, Lu C (2017) Rmpe: Regional multi-person pose estimation. In: IEEE International Conference on Computer Vision, pp 2334–2343

  6. Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Huang TS (2019) Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 6112–6121

  7. Gao M, Ai H, Bai B (2016) A feature fusion strategy for person re-identification. In: IEEE international conference on image processing, pp 4274–4278

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

  9. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv preprint arXiv:170307737

  10. Huang P, Huang R, Huang J, Yangchen R, He Z, Li X, Chen J (2019) Deep feature fusion with multiple granularity for vehicle re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 80–88

  11. Li D, Chen X, Zhang Z, Huang K (2017) Learning deep context-aware features over body and latent parts for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 384–393

  12. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: Deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 152–159

  13. Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2285–2294

  14. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2197–2206

  15. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp 740–755

  16. Navaneet K, Sarvadevabhatla R K, Shekhar S, Babu R V, Chakraborty A (2019) Operator-in-the-loop deep sequential multi-camera feature fusion for person re-identification. IEEE Transactions on Information Forensics and Security 15:2375–2385

    Article  Google Scholar 

  17. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. (2019) Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp 8024–8035

  18. Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision, pp 17–35

  19. Rui Z, Ouyang W, Wang X (2014) Learning mid-level filters for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 144–151

  20. Shen Y, Li H, Xiao T, Yi S, Chen D, Wang X (2018) Deep group-shuffling random walk for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2265–2274

  21. Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval. In: IEEE International Conference on Computer Vision, pp 3800–3808

  22. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: European Conference on Computer Vision, pp 480–496

  23. Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European Conference on Computer Vision, pp 791–808

  24. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, pp 5998–6008

  25. Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: ACM International Conference on Multimedia, pp 274–282

  26. Wang Y, Wang L, You Y, Zou X, Chen V, Li S, Huang G, Hariharan B, Weinberger KQ (2018) Resource aware person re-identification across multiple resolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 8042–8051

  27. Wei L, Zhang S, Yao H, Gao W, Tian Q (2017) Glad: Global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 25th ACM international conference on Multimedia, pp 420–428

  28. Xiang S, Fu Y, Xie M, Yu z, Liu T (2020) Unsupervised person re-identification by hierarchical cluster and domain transfer. MULTIMEDIA TOOLS AND APPLICATIONS

  29. Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: International Conference on Pattern Recognition, pp 34–39

  30. Zhang X, Luo H, Fan X, Xiang W, Sun Y, Xiao Q, Jiang W, Zhang C, Sun J (2017) Alignedreid: Surpassing human-level performance in person re-identification. arXiv preprint arXiv:171108184

  31. Zhao L, Li X, Zhuang Y, Wang J (2017) Deeply-learned part-aligned representations for person re-identification. In: IEEE International Conference on Computer Vision, pp 3219–3228

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

  33. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: Past, present and future. arXiv preprint arXiv:161002984

  34. Zheng M, Karanam S, Wu Z, Radke RJ (2019) Re-identification with consistent attentive siamese networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 5735–5744

  35. Zheng Z, Zheng L, Yang Y (2017) 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

  36. Zheng Z, Zheng L, Yang Y (2018) Pedestrian alignment network for large-scale person re-identification. IEEE Transactions on Circuits and Systems for Video Technology 29(10):3037–3045

    Article  Google Scholar 

  37. Zhong Z, Zheng L, Cao D, Li S (2017) Re-ranking person re-identification with k-reciprocal encoding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3652–3661

  38. Xiang S, Fu Y, You G, Liu T (2020) Unsupervised domain adaptation through synthesis for person re-identification. In: IEEE International Conference on Multimedia and Expo, pp 16

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Project(Grant No.61977045), the National Defense Pre-Research Foundation of China(Grant No.513110501). The authors would like to thank the anonymous reviewers for their valuable suggestions and constructive criticism.

Author information

Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Suncheng Xiang, Yuzhuo Fu, Hao Chen, Wei Ran & Ting Liu

Authors
  1. Suncheng Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuzhuo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Ran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suncheng Xiang.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, S., Fu, Y., Chen, H. et al. Multi-level feature learning with attention for person re-identification. Multimed Tools Appl 79, 32079–32093 (2020). https://doi.org/10.1007/s11042-020-09569-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09569-z

Keywords

Search

Navigation