An efficient global representation constrained by Angular Triplet loss for vehicle re-identification

Abstract

Vehicle re-identification is becoming an increasingly important problem in modern intelligent transportation systems. Substantial results have been achieved with methods based on deep metric learning. Most of the previous works tend to design complicated neural network models or utilize extra information. In this work, we introduce a simple Angular Triplet loss on the basis of analysis of different feature representations constrained by softmax loss and triplet loss. A batch normalization layer with zero bias is adopted to pass through the embedded feature before loss calculation. Then, triplet loss is calculated in cosine metric space instead of Euclidean space. In this way, triplet loss can cooperate with softmax consistently. By unifying the metric space of these two types of losses, the proposed method achieves 77.3% and 95.9% in rank-1 on VehicleID and VeRi-776 datasets, respectively. With only global features utilized, the proposed model can be seen as an effective baseline for vehicle re-identification task.

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References

  1. 1.

    Bai YX, Lou Y, Gao F, Wang S, Wu Y, Yu Duan L (2018) Group-sensitive triplet embedding for vehicle reidentification. IEEE Trans Multimed 20:2385–2399. https://doi.org/10.1109/TMM.2018.2796240

    Article  Google Scholar 

  2. 2.

    Bromley J, Bentz J, Bottou L, Guyon I, Lecun Y, Moore C, Sackinger E, Shah R (1993) Signature verification using a “siamese” time delay neural network. Int J Pattern Recognit Artif Intell 7(4):669–688. https://doi.org/10.1142/S0218001493000339

    Article  Google Scholar 

  3. 3.

    Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: The IEEE conference on computer vision and pattern recognition, pp 539–546. https://doi.org/10.1109/CVPR.2005.202

  4. 4.

    Chu R, Sun Y, Li Y, Liu Z, Zhang C, Wei, Y (2019) Vehicle re-identification with viewpoint-aware metric learning. In: The IEEE international conference on computer vision, pp 8282–8291. https://doi.org/10.1109/ICCV.2019.00837

  5. 5.

    Deng J, Dong W, Socher R, Li LJ, Li K, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  6. 6.

    Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface additive angular margin loss for deep face recognition. In: The IEEE conference on computer vision and pattern recognition, pp 4690–4699. https://doi.org/10.1109/CVPR.2019.00482

  7. 7.

    Feng Y, Yuan Y, Lu X (2019) Person reidentification via unsupervised cross-view metric learning. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2909480

    Article  Google Scholar 

  8. 8.

    He B, Li J, Zhao Y, Tian Y (2019) Part-regularized near-duplicate vehicle re-identification. In: The IEEE conference on computer vision and pattern recognition, pp 3997–4005. https://doi.org/10.1109/CVPR.2019.00412

  9. 9.

    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  10. 10.

    Kanacı A, Zhu X, Gong S (2018) Vehicle re-identification in context. In: German conference on pattern recognition. Springer, Berlin, pp 377–390. https://doi.org/10.1007/978-3-030-12939-2_26

  11. 11.

    Kingma D, Ba J (2014) Adam: a method for stochastic optimization. In: International conference on learning representations

  12. 12.

    Kumar R, Weill E, Aghdasi F, Sriram P (2019) Vehicle re-identification: an efficient baseline using triplet embedding. In: The international joint conference on neural networks, pp 1–9. https://doi.org/10.1109/IJCNN.2019.8852059

  13. 13.

    Li Y, Li Y, Yan H, Liu J (2017) Deep joint discriminative learning for vehicle re-identification and retrieval. In: The IEEE international conference on image processing, pp 395–399. https://doi.org/10.1109/ICIP.2017.8296310

  14. 14.

    Liu H, Tian Y, Wang Y, Pang L, Huang T (2016) Deep relative distance learning: tell the difference between similar vehicles. In: The IEEE conference on computer vision and pattern recognition, pp 2167–2175. https://doi.org/10.1109/CVPR.2016.238

  15. 15.

    Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: deep hypersphere embedding for face recognition. In: The IEEE conference on computer vision and pattern recognition, pp 6738–6746. https://doi.org/10.1109/CVPR.2017.713

  16. 16.

    Liu X, Liu W, Ma H, Fu H (2016) Large-scale vehicle re-identification in urban surveillance videos. In: The IEEE international conference on multimedia and expo, pp 1–6. https://doi.org/10.1109/ICME.2016.7553002

  17. 17.

    Liu X, Liu W, Mei T, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: European conference on computer vision, vol 9906. Springer, Berlin, pp 869–884. https://doi.org/10.1007/978-3-319-46475-6_53

  18. 18.

    Liu X, Liu W, Mei T, Ma H (2017) Provid: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20:645–658. https://doi.org/10.1109/TMM.2017.2751966

    Article  Google Scholar 

  19. 19.

    Liu X, Zhang S, Huang Q, Gao W (2018) Ram: a region-aware deep model for vehicle re-identification. In: The IEEE international conference on multimedia and expo, pp 1–6. https://doi.org/10.1109/ICME.2018.8486589

  20. 20.

    Lu X, Chen Y, Li X (2017) Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE Trans Image Process 27(1):106–120. https://doi.org/10.1109/TIP.2017.2755766

    MathSciNet  Article  MATH  Google Scholar 

  21. 21.

    Lv K, Du H, Hou Y, Deng W, Sheng H, Jiao J, Zheng L (2019) Vehicle re-identification with location and time stamps. In: The IEEE conference on computer vision and pattern recognition workshops

  22. 22.

    Manmatha R, Wu CY, Smola AJ, Krähenbühl P (2017) Sampling matters in deep embedding learning. In: The IEEE international conference on computer vision, pp 2859–2867. https://doi.org/10.1109/ICCV.2017.309

  23. 23.

    Movshovitz-Attias Y, Toshev A, Leung TK, Ioffe S, Singh S (2017) No fuss distance metric learning using proxies. In: The IEEE international conference on computer vision, pp 360–368. https://doi.org/10.1109/ICCV.2017.47

  24. 24.

    Oh Song H, Xiang Y, Jegelka S, Savarese S (2016) Deep metric learning via lifted structured feature embedding. In: The IEEE conference on computer vision and pattern recognition, pp 4004–4012. https://doi.org/10.1109/CVPR.2016.434

  25. 25.

    Ren J, Zhang C, Zhang L, Wang N, Feng Y (2018) Automatic measurement of traffic state parameters based on computer vision for intelligent transportation surveillance. Int J Pattern Recognit Artif Intell 32(4):1855003. https://doi.org/10.1142/S0218001418550030

    Article  Google Scholar 

  26. 26.

    Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: The IEEE conference on computer vision and pattern recognition, pp 815–823. https://doi.org/10.1109/CVPR.2015.7298682

  27. 27.

    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: The IEEE international conference on computer vision, pp 1900–1909. https://doi.org/10.1109/ICCV.2017.210

  28. 28.

    Sohn K (2016) Improved deep metric learning with multi-class n-pair loss objective. In: Advances in neural information processing systems, pp 1857–1865. https://doi.org/10.5555/3157096.3157304

  29. 29.

    Tang Z, Naphade M, Liu MY, Yang X, Birchfield S, Wang S, Kumar R, Anastasiu D, Hwang JN (2019) Cityflow: a city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In: The IEEE conference on computer vision and pattern recognition, pp 8797–8806. https://doi.org/10.1109/CVPR.2019.00900

  30. 30.

    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: The IEEE conference on computer vision and pattern recognition, pp 5265–5274. https://doi.org/10.1109/CVPR.2018.00552

  31. 31.

    Wang J, Zhou F, Wen S, Liu X, Lin Y (2017) Deep metric learning with angular loss. In: The IEEE international conference on computer vision, pp 2593–2601. https://doi.org/10.1109/ICCV.2017.283

  32. 32.

    Wang Z, Tang L, Liu X, Yao Z, Yi S, Shao J, Yan J, Wang S, Li H, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: The IEEE international conference on computer vision, pp 379–387. https://doi.org/10.1109/ICCV.2017.49

  33. 33.

    Wei XS, Zhang CL, Liu L, Shen C, Wu J (2018) Coarse-to-fine: a RNN-based hierarchical attention model for vehicle re-identification. In: Asian conference on computer vision. Springer, Berlin, pp 575–591. https://doi.org/10.1007/978-3-030-20890-5_37

  34. 34.

    Yang L, Luo P, Change Loy C, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: The IEEE conference on computer vision and pattern recognition, pp 3973–3981. https://doi.org/10.1109/CVPR.2015.7299023

  35. 35.

    Yang L, Luo P, Loy CC, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: The IEEE conference on computer vision and pattern recognition, pp 3973–3981. https://doi.org/10.1109/CVPR.2015.7299023

  36. 36.

    Zhang J, Wang FY, Wang K, Lin WH, Xu X, Chen C (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639. https://doi.org/10.1109/TITS.2011.2158001

    Article  Google Scholar 

  37. 37.

    Zhang Y, Liu D, Zha ZJ (2017) Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: The IEEE international conference on multimedia and expo, pp 1386–1391. https://doi.org/10.1109/ICME.2017.8019491

  38. 38.

    Zhou Y, Shao L (2018) Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: The IEEE conference on computer vision and pattern recognition, pp 6489–6498. https://doi.org/10.1109/CVPR.2018.00679

  39. 39.

    Zhu J, Zeng H, Huang J, Liao S, Lei Z, Cai C, Zheng L (2019) Vehicle re-identification using quadruple directional deep learning features. IEEE Trans Intell Transp Syst 21:410–420. https://doi.org/10.1109/TITS.2019.2901312

    Article  Google Scholar 

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Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant 61633019, the Science Foundation of Chinese Aerospace Industry under Grant JCKY2018204B053 and the Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT1917).

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Correspondence to Wei Jiang.

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Gu, J., Jiang, W., Luo, H. et al. An efficient global representation constrained by Angular Triplet loss for vehicle re-identification. Pattern Anal Applic 24, 367–379 (2021). https://doi.org/10.1007/s10044-020-00900-w

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Keywords

  • Vehicle re-identification
  • Angular Triplet loss
  • Metric learning