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In-depth exploration of attribute information for person re-identification

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Abstract

Pedestrian’s attribute information plays an important role in person re-identification (re-ID) for its complementary to pedestrian’s identity labels. However, there are few methods to utilize attribute information, which limits the development of re-ID community. In this paper, we analyze the effect of attribute information on re-ID to obtain both qualitative and quantitative results, indicating the potential for in-depth exploration of attribute information. On this basis, we propose an Identity Recognition Network (IRN) and an Attribute Recognition Network (ARN). IRN enhances the attention to pedestrian’s local information while identifying pedestrians’ identity. ARN calculates the attribute similarity among pedestrians accurately to promote the identification of IRN. The combination of them makes deep exploration of attribute information and is easy to implement. The experimental results on two large-scale re-ID benchmarks demonstrate the effectiveness of our method, which is on par with the state-of-the-art. In the DukeMTMC-reID dataset, mAP (rank-1) accuracy is improved from 58.4 (78.3) % to 66.4 (82.7) % for ResNet-50. In the Market1501 dataset, mAP (rank-1) accuracy is improved from 75.8 (90.5) % to 79.5 (92.8) % for ResNet-50.

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References

  1. Bai X, Yang M, Huang T, Dou Z, Yu R, Xu Y (2020) Deep-person: learning discriminative deep features for person re-identification. Pattern Recognition 98:107–036

    Google Scholar 

  2. Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 403–412

  3. Chen Y, Duffner S, Stoian A, Dufour J Y, Baskurt A (2018) Deep and low-level feature based attribute learning for person re-identification. Image Vis Comput 79:25–34

    Google Scholar 

  4. Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn 48(10):2993–3003

    Google Scholar 

  5. Franco A, Oliveira L (2017) Convolutional covariance features: conception, integration and performance in person re-identification. Pattern Recogn 61:593–609

    Google Scholar 

  6. Gao P, Yuan R, Wang F, Xiao L, Fujita H, Zhang Y (2019) Siamese attentional keypoint network for high performance visual tracking. Knowledge-Based Systems, pp 105448

  7. Gao P, Zhang Q, Wang F, Xiao L, Fujita H, Zhang Y (2020) Learning reinforced attentional representation for end-to-end visual tracking. Inf Sci 517:52–67

    Google Scholar 

  8. 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

  9. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv:170307737

  10. Jiang B, Wang X, Tang J (2019) Attkgcn: attribute knowledge graph convolutional network for person re-identification. arXiv:191110544

  11. Lan X, Zhu X, Gong S (2018) Person search by multi-scale matching. In: Proceedings of the European conference on computer vision (ECCV), pp 536–552

  12. Layne R, Hospedales TM, Gong S, Mary Q (2012) Person re-identification by attributes. In: Bmvc, vol 2, p 8

  13. Li A, Liu L, Wang K, Liu S, Yan S (2014) Clothing attributes assisted person reidentification. IEEE Transactions on Circuits and Systems for Video Technology 25(5):869–878

    Google Scholar 

  14. Li D, Chen X, Zhang Z, Huang K (2017) Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 384–393

  15. Li W, Zhu X, Gong S (2017) Person re-identification by deep joint learning of multi-loss classification. arXiv:170504724

  16. 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

  17. 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

  18. Lin Y, Zheng L, Zheng Z, Wu Y, Hu Z, Yan C, Yang Y (2019) Improving person re-identification by attribute and identity learning. Pattern Recogn

  19. Liu X, Song M, Zhao Q, Tao D, Chen C, Bu J (2012) Attribute-restricted latent topic model for person re-identification. Pattern Recogn 45(12):4204–4213

    Google Scholar 

  20. Liu Z, Wang J, Gong S, Lu H, Tao D (2019) Deep reinforcement active learning for human-in-the-loop person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 6122–6131

  21. Ma W, Han H, Zhang Y, Wang C (2018) Metric learning algorithm based on weighted pairwise constrained component analysis for person re-identification. In: 2018 IEEE 18th international conference on communication technology (ICCT). IEEE, pp 1154–1158

  22. Miao J, Wu Y, Liu P, Ding Y, Yang Y (2019) Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 542–551

  23. Qian X, Fu Y, Xiang T, Wang W, Qiu J, Wu Y, Jiang YG, Xue X (2018) Pose-normalized image generation for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 650–667

  24. 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

  25. 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

  26. Schumann A, Stiefelhagen R (2017) Person re-identification by deep learning attribute-complementary information. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 20–28

  27. Shen Y, Li H, Yi S, Chen D, Wang X (2018) Person re-identification with deep similarity-guided graph neural network. In: Proceedings of the European conference on computer vision (ECCV), pp 486–504

  28. Shen Y, Xiao T, Li H, Yi S, Wang X (2018) End-to-end deep kronecker-product matching for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6886–6895

  29. Si J, Zhang H, Li CG, Kuen J, Kong X, Kot AC, Wang G (2018) Dual attention matching network for context-aware feature sequence based person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5363–5372

  30. Siarohin A, Sangineto E, Lathuilière S, Sebe N (2018) Deformable gans for pose-based human image generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3408–3416

  31. Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. In: European conference on computer vision. Springer, pp 475–491

  32. Su C, Zhang S, Yang F, Zhang G, Tian Q, Gao W, Davis L S (2017) Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping. Pattern Recogn 66:4–15

    Google Scholar 

  33. Suh Y, Wang J, Tang S, Mei T, Mu Lee K (2018) Part-aligned bilinear representations for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 402–419

  34. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp 480–496

  35. Sun Y, Xu Q, Li Y, Zhang C, Li Y, Wang S, Sun J (2019) Perceive where to focus: learning visibility-aware part-level features for partial person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 393–402

  36. T Ali MF, Chaudhuri S (2018) Maximum margin metric learning over discriminative nullspace for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 122–138

  37. Tay CP, Roy S, Yap KH (2019) Aanet: attribute attention network for person re-identifications. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7134–7143

  38. Wang J, Zhu X, Gong S, Li W (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2275–2284

  39. Wang Z, Jiang J, Wu Y, Ye M, Bai X (2019) Learning sparse and identity-preserved hidden attributes for person re-identification. IEEE Trans Image Process, Satoh S

  40. Wu J, Zhao Y, Liu X (2018) Enhancing person retrieval with joint person detection, attribute learning, and identification. In: Pacific rim conference on multimedia. Springer, Berlin, pp 113–124

  41. Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288

    Google Scholar 

  42. Xiao Q, Luo H, Zhang C (2017) Margin sample mining loss: a deep learning based method for person re-identification. arXiv:171000478

  43. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1249–1258

  44. 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

  45. Yang W, Huang H, Zhang Z, Chen X, Huang K, Zhang S (2019) Towards rich feature discovery with class activation maps augmentation for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1389–1398

  46. Yao H, Zhang S, Hong R, Zhang Y, Xu C, Tian Q (2019) Deep representation learning with part loss for person re-identification. IEEE Trans Image Process 28(6):2860–2871

    MathSciNet  MATH  Google Scholar 

  47. Ye X, Wy Zhou, La Dong (2019) Body part-based person re-identification integrating semantic attributes. Neural Process Lett 49(3):1111–1124

    Google Scholar 

  48. Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: 2014 22nd international conference on pattern recognition. IEEE, pp 34–39

  49. Yu R, Dou Z, Bai S, Zhang Z, Xu Y, Bai X (2018) Hard-aware point-to-set deep metric for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 188–204

  50. Yu T, Li D, Yang Y, Hospedales TM, Xiang T (2019) Robust person re-identification by modelling feature uncertainty. In: Proceedings of the IEEE international conference on computer vision, pp 552–561

  51. Zhang Y, Gu X, Tang J, Cheng K, Tan S (2019) Part-based attribute-aware network for person re-identification. IEEE Access 7:53,585–53,595

    Google Scholar 

  52. Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X (2017) Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085

  53. 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

  54. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. arXiv:161002984

  55. Zheng L, Huang Y, Lu H, Yang Y (2019) Pose invariant embedding for deep person re-identification. IEEE Trans Image Process

  56. Zheng Z, Zheng L, Yang Y (2018) A discriminatively learned cnn embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14(1):13

    MathSciNet  Google Scholar 

  57. Zheng Z, Zheng L, Yang Y (2018) Pedestrian alignment network for large-scale person re-identification. IEEE Trans Circ Sys Video Technol

  58. Zhou K, Yang Y, Cavallaro A, Xiang T (2019) Omni-scale feature learning for person re-identification. arXiv:190500953

Download references

Acknowledgements

This study was funded by National Natural Science Foundation of China (grant number 61701029) and Basic Research Foundation of Beijing Institute of Technology (grant number 20170542008), and Industry-University-Research Innovation Foundation of the Science and Technology Development Center of the Ministry of Education (grant number 2018A02012).

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  1. School of Information and Electronics, Institute of Signal and Image Processing, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China

    Jianyuan Yin, Zheyi Fan, Shuni Chen & Yilin Wang

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  1. Jianyuan Yin
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Correspondence to Zheyi Fan.

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Yin, J., Fan, Z., Chen, S. et al. In-depth exploration of attribute information for person re-identification. Appl Intell 50, 3607–3622 (2020). https://doi.org/10.1007/s10489-020-01752-x

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