Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking | Science China Information Sciences Skip to main content
Springer Nature Link
Log in

Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Research on the application of vehicle re-identification to video surveillance has attracted increasingly growing attention. Existing methods are associated with the difficulties of distinguishing different instances of the same car model owing to the incapability of recognizing subtle differences among these instances and the possibility that a subtle difference may lead to incorrect results of ranking. In this paper, a discriminative fine-grained network for vehicle re-identification based on a two-stage re-ranking framework is proposed to address these issues. This discriminative fine-grained network (DFN) is composed of fine-grained and Siamese networks. The proposed hybrid network can extract discriminative features of the vehicle instances with subtle differences. The Siamese network is rather suitable for general object re-identification using two streams of the network, while the fine-grained network is capable of detecting subtle differences. The proposed two-stage re-ranking method allows obtaining a more reliable ranking list by using the Jaccard metric and merging the first and second re-ranking lists, where the latter list is formed using the sample mean feature. Experimental results on the VeRi-776 and VehicleID datasets show that the proposed method achieves the superior performance compared to the state-of-the-art methods used in vehicle re-identification.

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
¥17,985 /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 (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gou C, Wang K, Yao Y, et al. Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines. IEEE Trans Intell Transp Syst, 2016, 17: 1096–1107

    Article  Google Scholar 

  2. Min W, Li X, Wang Q, et al. New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification. IET Image Process, 2019, 13: 1041–1049

    Article  Google Scholar 

  3. Wang Y, Zhao C, Liu X, et al. Fast cartoon-texture decomposition filtering based license plate detection method. Math Problems Eng, 2018, 2018: 1–9

    Google Scholar 

  4. Wang T Q, Gong S G, Zhu X T, et al. Person re-identification by discriminative selection in video ranking. IEEE Trans Pattern Anal Mach Intell, 2016, 38: 2501–2514

    Article  Google Scholar 

  5. Zhao R, Oyang W L, Wang X G. Person re-identification by saliency learning. IEEE Trans Pattern Anal Mach Intell, 2017, 39: 356–370

    Article  Google Scholar 

  6. Cho Y J, Yoon K J. Improving person re-identification via pose-aware multi-shot matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016

  7. Zhao H Y, Tian M Q, Sun S Y, et al. Spindle net person re-identification with human body region guided. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017

  8. Feris R S, Siddiquie B, Petterson J, et al. Large-scale vehicle detection, indexing, and search in urban surveillance videos. IEEE Trans Multimedia, 2012, 14: 28–42

    Article  Google Scholar 

  9. Liu X C, Liu W, Ma H D, et al. Large-scale vehicle re-identification in urban surveillance videos. In: Proceedings of IEEE International Conference on Multimedia and Expo, 2016

  10. Loy C C, Xiang T, Gong S G. Multi-camera activity correlation analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009

  11. Shen Y T, Xiao T, Li H S, et al. Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In: Proceedings of IEEE International Conference on Computer Vision, 2017

  12. Wang Z D, Tang L M, Liu X H, et al. Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: Proceedings of IEEE International Conference on Computer Vision, 2017

  13. Gao Y, Beijbom O, Zhang N, et al. Compact bilinear pooling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016

  14. Matsukawa T, Okabe T, Suzuki E, et al. Hierarchical Gaussian descriptors with application to person re-identification. IEEE Trans Pattern Anal Mach Intell, 2019. doi: https://doi.org/10.1109/TPAMI.2019.2914686

  15. Chen D P, Yuan Z J, Chen B D, et al. Similarity learning with spatial constraints for person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016

  16. Zheng L, Shen L Y, Tian L, et al. Scalable person re-identification: a benchmark. In: Proceedings of IEEE International Conference on Computer Vision, 2015

  17. Varior R R, Wang G, Lu J W, et al. Learning invariant color features for person reidentification. IEEE Trans Image Process, 2016, 25: 3395–3410

    Article  MathSciNet  Google Scholar 

  18. Liao S C, Hu Y, Zhu X Y, et al. Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015

  19. Min W, Cui H, Rao H, et al. Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics. IEEE Access, 2018, 6: 9324–9335

    Article  Google Scholar 

  20. Liao Y, Xiong P, Min W, et al. Dynamic sign language recognition based on video sequence with BLSTM-3D residual networks. IEEE Access, 2019, 7: 38044–38054

    Article  Google Scholar 

  21. Min W, Fan M, Li J, et al. Real-time face recognition based on pre-identification and multi-scale classification. IET Comput Vision, 2019, 36: 165–171

    Article  Google Scholar 

  22. Zhang K, Liu N, Yuan X F, et al. Fine-grained age estimation in the wild with attention LSTM networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018

  23. Ji Z, Xiong K, Pang Y, et al. Video summarization with attention-based encoder-decoder networks. IEEE Trans Circ Syst Video Technol, 2020, 30: 1709–1717

    Article  Google Scholar 

  24. Ji Z, Sun Y, Yu Y, et al. Attribute-guided network for cross-modal zero-shot hashing. IEEE Trans Neural Netw Learn Syst, 2020, 31: 321–330

    Article  Google Scholar 

  25. Zhao R, Ouyang W L, Wang X G. Learning mid-level filters for person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014

  26. Zheng Z D, Zheng L, Yang Y. A discriminatively learned CNN embedding for person reidentification. ACM Trans Multimedia Comput Commun Appl, 2018, 14: 1–20

    Article  Google Scholar 

  27. Cheng D, Gong Y H, Zhou S P, et al. Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016

  28. Xu S J, Cheng Y, Gu K, et al. Jointly attentive spatial-temporal pooling networks for video-based person reidentification. In: Proceedings of IEEE International Conference on Computer Vision, 2017

  29. Zhao C R, Chen K, Zang D, et al. Uncertainty-optimized deep learning model for small-scale person re-identification. Sci China Inf Sci, 2019, 62: 220102

    Article  Google Scholar 

  30. Paisitkriangkrai S, Shen C H, Hengel A V D. Learning to rank in person re-identification with metric ensembles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015

  31. Yi D, Zhen L, Liao S C, et al. Deep metric learning for person re-identification. In: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 2014

  32. Liu H Y, Tian Y H, Wang Y W, et al. Deep relative distance learning: tell the difference between similar vehicles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016

  33. Tang Y, Wu D, Jin Z, et al. Multi-modal metric learning for vehicle re-identification in traffic surveillance environment. In: Proceedings of IEEE International Conference on Image Processing, 2017

  34. Zapletal D, Herout A. Vehicle re-identification for automatic video traffic surveillance. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016

  35. Yang L J, Lou P, Loy C C, et al. A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015

  36. Zhang Y H, Liu D, Zha Z J. Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: Proceedings of IEEE International Conference on Mutimedia and Expo, 2017

  37. Zhou Y, Shao L. Vehicle re-identification by adversarial bi-directional LSTM network. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, 2018

  38. Zhou Y, Shao L. Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018

  39. Zhou Y, Shao L. Cross-view GAN based vehicle generation for re-identification. In: Proceedings of British Machine Vision Conference, 2017

  40. Zhu J Q, Zeng H Q, Jin X, et al. Joint horizontal and vertical deep learning feature for vehicle re-identification. Sci China Inf Sci, 2019, 62: 199101

    Article  Google Scholar 

  41. Martin K, Hirze M, Wohihart P, et al. Large scale metric learning from equivalence constraints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012

  42. Li Z C, Tang J H. Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimedia, 2015, 17: 1989–1999

    Article  Google Scholar 

  43. Zhong Z, Zheng L, Cao D L, et al. Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017

  44. Ding S Y, Lin L, Wang G R, et al. Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn, 2015, 48: 2993–3003

    Article  Google Scholar 

  45. Li Z, Chang S Y, Liang F, et al. Learning locally-adaptive decision functions for person verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013

  46. Valev K, Schumann A, Sommer L, et al. A systematic evaluation of recent deep learning architectures for fine-grained vehicle classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018

  47. Ma Z, Chang D, Li X. Channel max pooling layer for fine-grained vehicle classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019

  48. Wang Q, Ding Y D. A novel fine-grained method for vehicle type recognition based on the locally enhanced PCANet neural network. J Comput Sci Technol, 2018, 33: 335–350

    Article  Google Scholar 

  49. Yu S, Wu Y, Li W, et al. A model for fine-grained vehicle classification based on deep learning. Neurocomputing, 2017, 257: 97–103

    Article  Google Scholar 

  50. Hu B, Lai J H, Guo C C. Location-aware fine-grained vehicle type recognition using multi-task deep networks. Neurocomputing, 2017, 243: 60–68

    Article  Google Scholar 

  51. Zhang Q, Zhuo L, Hu X, et al. Fine-grained vehicle recognition using hierarchical fine-tuning strategy for urban surveillance videos. In: Proceedings of International Conference on Progress in Informatics and Computing, 2017

  52. Liu X C, Liu W, Mei T, et al. A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Proceedings of Europeon Conference on Computer Vision, 2016

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61762061, 62076117), National Key R&D Program of China (Grant Nos. 2017YFB0801701, 2017YFB0802805), Natural Science Foundation of Jiangxi Province (Grant No. 20161ACB20004), and Jiangxi Key Laboratory of Smart City (Grant No. 20192BCD40-002).

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Qi Wang, Song Zou, Tiemei Huang, Yu Zhang & Ruikang Liu

  2. School of Software, Nanchang University, Nanchang, 330047, China

    Weidong Min

  3. Jiangxi Key Laboratory of Smart City, Nanchang University, Nanchang, 330047, China

    Weidong Min

  4. School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China

    Daojing He

Authors
  1. Qi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weidong Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daojing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tiemei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ruikang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weidong Min.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Min, W., He, D. et al. Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking. Sci. China Inf. Sci. 63, 212102 (2020). https://doi.org/10.1007/s11432-019-2811-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2811-8

Keywords

Search

Navigation