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A siamese pedestrian alignment network for person re-identification

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Abstract

Deep learning methods show strong ability in extracting high-level features for images in the field of person re-identification. The produced features help inherently distinguish pedestrian identities in images. However, on deep learning models over-fitting and discriminative ability of the learnt features are still challenges for person re-identification. To alleviate model over-fitting and further enhance the discriminative ability of the learnt features, we propose siamese pedestrian alignment networks (SPAN) for person re-identification. SPAN employs two streams of PAN (pedestrian alignment networks) to increase the size of network inputs over limited training samples and effectively alleviate network over-fitting in learning. In addition, a verification loss is constructed between the two PANs to adjust the relative distance of two input pedestrians of the same or different identities in the learned feature space. Experimental verification is conducted on six large person re-identification data sets and the experimental results demonstrate the effectiveness of the proposed SPAN for person re-identification.

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References

  1. Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3908–3916

  2. Bai S, Bai X, Tian Q (2017) Scalable person re-identification on supervised smoothed manifold. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2530–2539

  3. Cao J, Pang Y, Han J, Gao B, Li X (2019) Taking a look at small-scale pedestrians and occluded pedestrians. IEEE Trans Image Process 29:3143–3152

    Article  Google Scholar 

  4. Chen D, Yuan Z, Hua G, Zheng N, Wang J (2015) Similarity learning on an explicit polynomial kernel feature map for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1565–1573

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

  6. Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Proceedings of the iEEE conference on computer vision and pattern recognition, pp 1335–1344

  7. Cheng DS, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification.. In: Bmvc, vol 1, p 6

  8. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  9. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE international workshop on performance evaluation for tracking and surveillance (PETS), vol 3. Citeseer, pp 1–7

  10. Han J, Pauwels EJ, de Zeeuw PM, de With PHN (2012) Employing a rgb-d sensor for real-time tracking of humans across multiple re-entries in a smart environment. IEEE Trans Consum Electron 58(2):255–263

    Article  Google Scholar 

  11. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  12. He L, Liang J, Li H, Sun Z (2018) Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7073–7082

  13. Hu J, Lu J, Tan Y-P (2014) Discriminative deep metric learning for face verification in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  14. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  15. Huang G, Mattar M, Lee H, Learned-Miller EG (2012) Learning to align from scratch. In: Advances in neural information processing systems, pp 764–772

  16. Huang Y, Xu J, Wu Q, Zheng Z, Zhang Z, Zhang J (2019) Multi-pseudo regularized label for generated data in person re-identification. IEEE Trans Image Process 28(3):1391–1403

    Article  MathSciNet  Google Scholar 

  17. Karanam S, Gou M, Wu Z, Rates-Borras A, Camps O, Radke RJ (2016) A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets. arXiv:1605.09653

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  19. Li D, Zhang Z, Chen X, Ling H, Huang K (2016) A richly annotated dataset for pedestrian attribute recognition. arXiv:1603.07054

  20. Li W, Zhao R, Wang X (2012) Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision. Springer, pp 31–44

  21. Li W, Zhu X, Gong S (2017) Person re-identification by deep joint learning of multi-loss classification. arXiv:1705.04724

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

  23. Liao S, Li SZ (2015) Efficient psd constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3685–3693

  24. Liu H, Feng J, Qi M, Jiang J, Yan S (2017) End-to-end comparative attention networks for person re-identification. IEEE Trans Image Process 26(7):3492–3506

    Article  MathSciNet  Google Scholar 

  25. Ma AJ, Yuen PC, Li J (2013) Domain transfer support vector ranking for person re-identification without target camera label information. In: Proceedings of the IEEE international conference on computer vision, pp 3567–3574

  26. Ma L, Liu H, Hu L, Wang C, Sun Q (2016) Orientation driven bag of appearances for person re-identification. arXiv:1605.02464

  27. Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  28. Pang Y, Cao J, Wang J, Han J (2019) Jcs-net: Joint classification and super-resolution network for small-scale pedestrian detection in surveillance images. IEEE Trans Inf Forensic Secur 14(12):3322–3331

    Article  Google Scholar 

  29. Radenović F, Tolias G, Chum O (2016) Cnn image retrieval learns from bow: Unsupervised fine-tuning with hard examples. In: European conference on computer vision. Springer, pp 3–20

  30. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  31. 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. Springer, pp 17–35

  32. Ristani E, Tomasi C (2018) Features for multi-target multi-camera tracking and re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6036–6046

  33. Saquib Sarfraz M, Schumann A, Eberle A, Stiefelhagen R (2018) A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 420–429

  34. Shen Y, Lin W, Yan J, Xu M, Wu J, Wang J (2015) Person re-identification with correspondence structure learning. In: Proceedings of the IEEE international conference on computer vision, pp 3200–3208

  35. Shi Z, Hospedales TM, Xiang T (2015) Transferring a semantic representation for person re-identification and search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4184–4193

  36. Su C, Li J, Zhang S, Xing J, Gao W, Tian Q (2017) Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3960–3969

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

  38. Tan S, Zheng F, Liu L, Han J, Shao L (2016) Dense invariant feature-based support vector ranking for cross-camera person reidentification. IEEE Trans Circ Syst Video Technol 28(2):356–363

    Article  Google Scholar 

  39. Varior RR, Shuai B, Lu J, Xu D, Wang G (2016) A siamese long short-term memory architecture for human re-identification. In: European Conference on Computer Vision. Springer, pp 135–153

  40. Vedaldi A, Lenc K (2015) Matconvnet: Convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on Multimedia. ACM, pp 689–692

  41. Wei L, Zhang S, Gao W, Tian Q (2018) Person trasfer gan to bridge domain gap for person re-identification. In: Computer Vision and Pattern Recognition, IEEE Conference on

  42. Xu J, Zhao R, Zhu F, Wang H, Ouyang W (2018) Attention-aware compositional network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2119–2128

  43. Yang Y, Liao S, Lei Z, Li SZ (2016) Large scale similarity learning using similar pairs for person verification. In: Thirtieth AAAI conference on artificial intelligence

  44. Yang Y, Yang J, Yan J, Liao S, Yi D, Li SZ (2014) Salient color names for person re-identification. In: European conference on computer vision. Springer, pp 536–551

  45. Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, pp 34–39

  46. Yu R, Zhou Z, Bai S, Bai X (2017) Divide and fuse: A re-ranking approach for person re-identification. arXiv:1708.04169

  47. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1239–1248

  48. Zhao J, Fernandes VB, Jiao L, Yevseyeva I, Maulana A, Li R, Bck T, Tang K, Emmerich MTM (2016) Multiobjective optimization of classifiers by means of 3d convex-hull-based evolutionary algorithms. Inf Sci 367-368:80–104

    Article  Google Scholar 

  49. Zhao J, Jiao L, Liu F, Fernandes VB, Yevseyeva I, Xia S, Emmerich MTM (2018) 3D fast convex-hull-based evolutionary multiobjective optimization algorithm. Appl Soft Comput 67:322–336

    Article  Google Scholar 

  50. Zheng L, Bie Z, Sun Y, Wang J, Su C, Wang S, Tian Q (2016) Mars: A video benchmark for large-scale person re-identification. In: European Conference on Computer Vision. Springer, pp 868–884

  51. Zheng L, Huang Y, Lu H, Yang Y (2019) Pose-invariant embedding for deep person re-identification. IEEE Trans Image Process 28(9):4500–4509

    Article  MathSciNet  Google Scholar 

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

  53. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: Past, present and future. arXiv:1610.02984

  54. Zheng W-S, Gong S, Xiang T (2012) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668

    Article  Google Scholar 

  55. Zheng Z, Zheng L, Yang Y (2017) A discriminatively learned cnn embedding for person reidentification. ACM Trans Multimed Comput Commun Appl (TOMM) 14(1):13:1–13:20

    Google Scholar 

  56. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. arXiv:1701.07717.3

  57. 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

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

  59. Zhong Z, Zheng L, Zheng Z, Li S, Yang Y (2018) Camera style adaptation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5157–5166

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their very competent comments and suggestions. An earlier version of this paper was presented at the Chinese Conference on Pattern Recognition and Computer Vision(PRCV 2019).

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No.2018XKQYMS27).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Yi Zheng, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu & Ying Chen

  2. Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Yi Zheng, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu & Ying Chen

  3. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Meng Jian

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  1. Yi Zheng
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Correspondence to Yong Zhou.

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Zheng, Y., Zhou, Y., Zhao, J. et al. A siamese pedestrian alignment network for person re-identification. Multimed Tools Appl 80, 33951–33970 (2021). https://doi.org/10.1007/s11042-021-11302-3

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