Skip to main content
SpringerLink
Log in

Efficient but lightweight network for vehicle re-identification with center-constraint loss

  • S.I.: Machine Learning based semantic representation and analytics for multimedia application
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Vehicle re-identification aims to retrieve the target vehicle from the image gallery quickly and accurately. Vehicle re-identification with deep learning has achieved considerable performance. However, most popular methods need construct complex network, which increases the calculation of network and difficulty of training. To balance performance and complexity, an efficient but lightweight network is proposed in this work. The designed network utilizes the global branch and the mask branch to extract the feature. The former can extract global feature efficiently. The latter can remove the changeable background based on the proposed mask-mapping module, which can map mask to feature map and adjust feature map dynamically. And then, the features from the original image and the mask-mapping module are fused to generate the final feature for vehicle re-identification. Besides, a novel center-constraint triplet loss is designed to optimize the proposed network and excavate more discriminate feature. Different from triplet loss, the proposed loss can consider more extra samples and constrain the center from positive sample set as well as negative sample set. To enhance the difference between hard samples and simple samples, an unequal weight strategy is embedded in this loss. The proposed method achieves 78.7% mAP with 95.4% Rank-1 on VeRi-776, and 84.3%, 80.7%, and 80.1% Rank-1 on three subsets from VehicleID, which demonstrates the effectiveness of the proposed method.

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

Similar content being viewed by others

References

  1. Liu X, Liu W, Ma H, Fu H (2016) Large-scale vehicle re-identification in urban surveillance videos. In: IEEE International conference on multimedia and expo, pp. 1–6

  2. Liu X, Wu L, Tao M, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: European conference on computer vision, pp. 869–884

  3. Chao S, Nong S, Gao C, Lei Z(2017) Vehicle re-identification by fusing multiple deep neural networks. In: International conference on image processing theory, tools and applications, pp. 1–6

  4. Zhong K, Wang Y, Pei J, Tang S, Han Z (2021) Super efficiency SBM-DEA and neural network for performance evaluation. Inf Process Manag 58(6):102728

    Article  Google Scholar 

  5. Wang Z, Tang L, Liu X, Yao Z, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: IEEE International conference on computer vision, pp. 379–387

  6. Ma X, Zhu K, Guo H, Wang J, Miao Q (2019) Vehicle re-identification with refined part model. In: IEEE International conference on multimedia and expo workshops, pp. 603–606

  7. Schroff F, Kalenichenko D, Facenet Philbin. J (2015) A unified embedding for face recognition and clustering. In: IEEE Conference on computer vision and pattern recognition, pp. 815–823

  8. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification

  9. Li Y, Li Y, Yan H, Liu J (2017) Deep joint discriminative learning for vehicle re-identification and retrieval. In: IEEE International conference on image processing, pp. 395–399

  10. Tang Y, Wu D, Jin Z, Zou W, Li X (2017) Multi-modal metric learning for vehicle re-identification in traffic surveillance environment. In: IEEE International conference on image processing, pp. 2254–2258

  11. Tang Z, Naphada M, Birchfiels S, Tremblay J, Hodge W, Kumar R, Wang S, Yang X (2019) Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In: IEEE CVF International conference on computer vision, pp. 211–220

  12. Gupta A, Chittrarasu A, Balakrishnan R (2020) Vehicle re-identification using relative entropy representation and gated recurrent unit networks. Procedia Comput Sci 171:508–513

    Article  Google Scholar 

  13. Teng S, Zhang S, Huang Q, Sebe N (2021) Viewpoint and scale consistency reinforcement for UAV vehicle re-identification. Int J Comput Vision 129:719–735

    Article  Google Scholar 

  14. Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cosface: Large margin cosine loss for deep face recognition. In IEEE CVF Conference on Computer Vision and Pattern Recognition, pp. 5265–5274

  15. Sun Y, Cheng C, Zhang Y, Zhang C, Zheng L, Wang Z, Wei Y (2020) Circle loss: a unified perspective of pair similarity optimization. In: IEEE CVF conference on computer vision and pattern recognition, pp. 6397–6406

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

  17. Guo H, Zhao C, Liu Z, Wang J, Lu H (2018) Learning coarse-to-fine structured feature embedding for vehicle re-identification. In: AAAI Conference on Artificial Intelligence, pp. 6853–6860

  18. Gu J, Jiang W, Luo H, Yu H (2021) An efficient global representation constrained by angular triplet loss for vehicle re-identification. Pattern Anal Appl 24(1):367–379

    Article  Google Scholar 

  19. Chen X, Sui H, Fang J, Zhou M, Wu C (2020) Multi-proxy constraint loss for vehicle re-identification. Sensors 20(18):1–15

    Article  Google Scholar 

  20. Zhong Z, Zheng L, Kang G, Li S, Yang L (2017) Random erasing data augmentation. Proc. AAAI Conf. Artif. Intell. 34:13001–13008

    Google Scholar 

  21. 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

  22. He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In IEEE International conference on computer vision, pp. 2980–2988

  23. Liu W, Wen Y, Yu Z, Yang M (2016) Large-margin softmax loss for convolutional neural networks. In: International conference on machine learning, pp. 507–516

  24. Liu W, Wen Y, Yu Z, Li M, Raj B, Song L(2017) Sphereface: Deep hypersphere embedding for face recognition. In: IEEE Conference on computer vision and pattern recognition, pp. 6738–6746

  25. Wang F, Cheng J, Liu W, Liu H (2018) Additive margin softmax for face verification. IEEE Signal Processing Letters 25(7):926–930

    Article  Google Scholar 

  26. Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: A deep quadruplet network for person re-identification. In: IEEE Conference on computer vision and pattern recognition, pp. 1320–1329

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

    Article  Google Scholar 

  28. Liu H, Tian Y, Wang Y, Pang L, Huang T (2016) Deep relative distance learning: tell the difference between similar vehicles. In: IEEE Conference on computer vision and pattern recognition, pp. 2167–2175

  29. Pei J, Zhong K, Li J, Xu J, Wang X (2021) ECNN: evaluating a cluster-neural network model for city innovation capability. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06471-z

    Article  Google Scholar 

  30. Teng S, Liu X, Zhang S, Huang Q (2018) Scan: spatial and channel attention network for vehicle re-identification. In Pacific rim conference on multimedia, pp. 350–361

  31. Shen Y, Xiao T, Li H, Yi S, Wang X (2017) Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In: IEEE International conference on computer vision, pp. 1918–1927

  32. Lou Y, Bai Y, Liu J, Wang S, Duan. Veri-wild L (2019) A large dataset and a new method for vehicle re-identification in the wild. In: IEEE CVF Conference on Computer Vision and Pattern Recognition, pp. 3230–3238

  33. Chun R, Sun Y, Li Y, Liu Z, Zhang C, Wei Y (2019) Vehicle re-identification with viewpoint-aware metric learning. In: IEEE CVF International conference on computer vision, pp. 8281–8290

  34. He B, Li J, Zhao Y, Tian Y (2019) Part-regularized near-duplicate vehicle re-identification. In: IEEE CVF Conference on computer vision and pattern recognition, pp. 3992–4000

  35. Zhu R, Fang J, Xu H, Yu H, Xue J (2020) DCDLearn: multi-order deep cross-distance learning for vehicle re-identification

  36. Hou J, Zeng H, Cai L, Zhu J, Chen J, Ma K (2019) Multi-label learning with multi-label smoothing regularization for vehicle re-identification. Neurocomputing 345:15–22

    Article  Google Scholar 

  37. Zheng A, Lin X, Li C, He R, Tang J (2019) Attributes guided feature learning for vehicle re-identification

  38. He X, Zhou Y, Zhou Z, Bai S, Bai X (2018) Triplet-center loss for multi-view 3D object retrieval. In: IEEE CVF conference on computer vision and pattern recognition, pp. 1945–1954

Download references

Author information

Authors and Affiliations

  1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Zhi Yu

  2. D-BAUG, Swiss Federal Institute of Technology in ETH Zurich, Zurich, Switzerland

    Mingpeng Zhu

Authors
  1. Zhi Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingpeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi Yu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest exists.

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

Yu, Z., Zhu, M. Efficient but lightweight network for vehicle re-identification with center-constraint loss. Neural Comput & Applic 34, 12373–12384 (2022). https://doi.org/10.1007/s00521-021-06658-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06658-4

Keywords

Search

Navigation