Skip to main content
SpringerLink
Log in

Dual-relational attention network for vehicle re-identification

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The vehicle re-identification task aims to retrieve the same vehicle from multiple cameras. One of the challenges of this task is that images of different vehicles may look very similar under the same perspective, which affects the performance of vehicle re-identification tasks. Recent studies have shown that the attention mechanism is effective for vehicle re-identification, because it encourages models to focus on information related to the identity of the target rather than interference information such as background. In this paper, we first propose a dual-relational attention module (DRAM), which simultaneously constructs the importance of a feature point in the spatial and channel dimensions, and further models the attention of the feature point in the three-dimensional space. Then, we integrate the dual-relational attention module into a three-branch network, called dual-relational attention network (DRA-Net). In addition, to ensure that the network can extract a large number of diverse information, we add a non-similarity constraint in dual-relational attention network (DRA-Net) to make different branches focus on different locations in an image. In the experiments, our method is verified on two datasets (VeRi-776 and VehicleID), and the experimental results indicate that the proposed method is superior to several advanced methods.

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

Similar content being viewed by others

Data Availability

All data included in this study are available upon request by contact with the corresponding author.

References

  1. Chen T, Ding S, Xie J, et al. (2019) ABD-Net: Attentive but Diverse Person Re-Identification. In: IEEE international conference on computer vision (ICCV), pp 8350–8360

  2. Woo S, Park J, Lee J, et al. (2018) CBAM: Convolutional Block Attention Module. In: European conference on computer vision (ECCV), pp 3–19

  3. Liu K, Xu Z, Hou Z, et al. (2020) Further Non-local and Channel Attention Networks for Vehicle Re-identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 2494–2500

  4. Bryan X, Gong Y et al (2019) Second-order Non-local Attention Networks for Person Re-identification. In: IEEE international conference on computer vision (ICCV), pp 3759–3768

  5. Wang X, Girshick R, Gupta A et al (2018) Non-local Neural Networks. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 7794–7803

  6. Zhang Z, Lan C, Zeng W et al (2020) Relation-Aware Global Attention for Person Re-Identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 3183–3192

  7. Liu H, Tian Y, Wang Y et al (2016) Deep Relative Distance Learning: Tell the Difference between Similar Vehicles. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 2167–2175

  8. Ke Y, Tian Y, Wang Y et al (2017) Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles. In: IEEE international conference on computer vision (ICCV), pp 562–570

  9. Wang Z, Tang L, Liu X et al (2017) Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification. In: IEEE international conference on computer vision (ICCV), pp 379–387

  10. Shen Y, Tong X, Li H et al (2017) Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals. In: IEEE international conference on computer vision (ICCV), pp 1918–1927

  11. Liu X, Wu L, Tao M et al (2016) A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance. In: European conference on computer vision (ECCV), pp 869–884

  12. Zapletal D, Herout A (2016) Vehicle Re-identification for Automatic Video Traffic Surveillance. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 1568–1574

  13. Liu W, Wen Y, Yu Z et al (2016) Large-Margin Softmax Loss for Convolutional Neural Networks. In: Thirty-Eighth International Conference on Machine Learning(ICML), pp 507–516

  14. Hermans A, Beyer L, Leibe B (2017) In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737

  15. Zheng Z, Zheng L, Garrett M et al (2020) Dual-Path Convolutional Image-Text Embedding. In: ACM Transactions on Multimedia Computing, Communications, and Applications, pp 51:1– 51:23

  16. Zhang Y, Dong L, Zha Z (2017) Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: IEEE international conference on multimedia and expo(ICME), pp 1386–1391

  17. Yuan Y, Yang K, Zhang C (2017) Hard-Aware Deeply Cascaded Embedding. In: IEEE international conference on computer vision (ICCV), pp 814–823

  18. Xu Q, Yan K, Tian Y (2017) Learning a Repression Network for Precise Vehicle Search. arXiv:1708.02386

  19. Zhou Y, Shao L (2017) Cross-View GAN Based Vehicle Generation for Re-identification. In: British Machine Vision Conference(BMVC)

  20. Yi Z, Ling S (2018) Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 6489–6498

  21. Fei W, Jiang M, Chen Q et al (2017) Residual Attention Network for Image Classification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 6450–6458

  22. Lin C, Lu J, Wang G et al (2018) Graininess-Aware Deep Feature Learning for Pedestrian Detection. In: European conference on computer vision (ECCV), pp 745–761

  23. Jie H, Li S, Gang S et al (2018) Squeeze-and-Excitation Networks. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 7132–7141

  24. Zhang Y, Li K, Li K et al (2018) Image Super-Resolution Using Very Deep Residual Channel Attention Networks. In: European conference on computer vision (ECCV), pp 294–310

  25. Liu H, Feng J, Qi M et al (2017) End-to-End Comparative Attention Networks for Person Re-Identification. In: IEEE transactions on image processing, pp 3492–3506

  26. Cheng W, Qian Z, Chang H et al (2018) Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification. In: European conference on computer vision (ECCV), pp 384–400

  27. Gao S, Wang J, Lu, H et al (2020) Pose-guided Visible Part Matching for Occluded Person ReID. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 11741–11749

  28. Zhang X, Luo H, Fan X et al (2017) AlignedReID: Surpassing Human-Level Performance in Person Re-Identification. arXiv:1711.08184

  29. Sun Y, Zheng L, Yang Y et al (2018) Beyond Part Models: Person Retrieval with Refined Part Pooling. In: European conference on computer vision (ECCV), pp 501–518

  30. Bai X, Yang M, Huang T et al (2017) Deep-Person: Learning Discriminative Deep Features for Person Re-Identification. In: Pattern Recognition

  31. Kingma D, Ba J (2015) Adam: A Method for Stochastic Optimization. In: Tenth international conference on learning representations(ICLR)

  32. Lou Y, Bai Y, Liu J et al (2019) Embedding Adversarial Learning for Vehicle Re-Identification. In: IEEE Transactions on Image Processing, pp 1–1

  33. Liu X, Zhang S, Huang Q et al (2018) RAM: A Region-Aware Deep Model for Vehicle Re-Identification. In: IEEE international conference on multimedia and expo(ICME), pp 1–6

  34. Khorramshahi P, Kumar A, Peri N et al (2019) A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification. In: IEEE international conference on computer vision (ICCV), pp 6131–6140

  35. Kanaci A, Li M, Gong S et al (2019) Multi-Task Mutual Learning for Vehicle Re-Identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 62–70

  36. Zhu J, Zeng H, Huang J et al (2019) Vehicle Re-Identification Using Quadruple Directional Deep Learning Features. In: IEEE Transactions on Intelligent Transportation Systems, pp 1–11

  37. Chen T, Lee M, Liu C et al (2020) Viewpoint-Aware Channel-Wise Attentive Network for Vehicle Re-Identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 2448–2455

  38. He B, Li J, Zhao Y et al (2019) Part-Regularized Near-Duplicate Vehicle Re-Identification. In: IEEE conference on computer vision and pattern recognition(CVPR), pp 3997–4005

  39. Wang H, Peng J, Jiang G et al (2021) Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification. Neurocomputing 438(12)

  40. Peng J, Jiang G, Wang H (2021) Generalized multiple sparse information fusion for vehicle re-identification. J Vis Commun Image Represent 79:103207

    Article  Google Scholar 

  41. Teng S, Zhang S, Huang Q et al (2021) Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification. Int J Comput Vis 129(3):719–735

    Article  Google Scholar 

  42. Fu X, Peng J, Jiang G et al (2022) Learning latent features with local channel drop network for vehicle re-identification. Eng Appl Artif Intell 107:104540

    Article  Google Scholar 

  43. Du L, Yu C, Shuai C et al (2021) A Multiscale Attention Mechanism Based Vehicle Re-Identification. ICSAI:1–6

  44. Qian J, Jiang W, Luo H et al (2019) Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification. arXiv:1910.05549

  45. Khorramshahi P, Peri N, Chen J et al (2020) The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification. In: European conference on computer vision (ECCV), pp 369– 386

  46. Li K, Ding Z, Li K, et al. (2022) Vehicle and Person Re-Identification With Support Neighbor Loss. IEEE Trans Neural Netw Learn Syst 33(2):826–838

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Focus on Research and Development Plan in Shandong Province NO.2019GGX101055, and the Shandong Education Science “13th five year plan” special project “Research on examinee online identity verification system based on multimodal biometrics” NO. BYZK201904.

Author information

Authors and Affiliations

  1. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China

    Yanli Zheng, Xiyu Pang, Gangwu Jiang, Xin Tian & Qinglan Meng

Authors
  1. Yanli Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiyu Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gangwu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qinglan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiyu Pang.

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

Zheng, Y., Pang, X., Jiang, G. et al. Dual-relational attention network for vehicle re-identification. Appl Intell 53, 7776–7787 (2023). https://doi.org/10.1007/s10489-022-03801-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03801-z

Keywords

Search

Navigation