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Person re-identification based on activation guided identity and attribute classification model

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Abstract

Attribute features can be exploited as high-level semantic features to help the model better express person characteristics and improve person re-identification (re-ID) performance. In this paper, a novel activation guided identity and attribute classification (AGIAC) model is proposed for person re-ID. AGIAC model includes the backbone, the mutex local activation (MLA) module and the branch-guided identity-attribute classification (BIAC) module. To fuse the global and local features, the backbone is designed as four branches. Based on the fact the different attributes are related to different regions, the BIAC module is designed, which employs the relevant branch to classify each attribute. To provide more specific information for each attribute in the BIAC model and increase the feature diversity of the four branches, the MLA module is constructed, which generates mutex activation area for each branch. The overall loss function of the AGIAC model is designed as the combination of identity classification loss, attribute classification loss and activation loss. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that the AGIAC model outperforms the state-of-the-art attribute-combined re-ID methods.

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References

  1. Chen T, Ding S, Xie J, Yuan Y, Chen W, Yang Y, Ren Z, Wang Z (2019) ABD-Net: attentive but diverse person re-identification, in IEEE International Conference on Computer Vision (ICCV), pp 8351–8361

  2. Chikontwe P, Lee HJ (2018) Deep multi-task network for learning person identity and attributes. IEEE Access 6:60801–60811

    Article  Google Scholar 

  3. Fu Y, Wei Y, Zhou Y, Shi H, Huang G, Wang X, Yao Z, Huang TS (2019) Horizontal pyramid matching for person re-identification. Natl Conf Artif Intell 33(01):8295–8302

    Google Scholar 

  4. Gong K, Liang X, Zhang D, Shen X, Lin L (2017) Look into person: self-supervised structure-sensitive learning and a new benchmark for human parsing, in arXiv preprint, arXiv:1703.05446

  5. Han K, Guo J, Zhang C, Zhu M (2018) Attribute-aware attention model for fine–grained representation learning, in the 26th ACM International Conference on Multimedia, 2040–2048

  6. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 770–778

  7. Huang H, Chen X, Huang K (2020) Human parsing based alignment with multi-task learning for occluded person re-identification, in IEEE International Conference on Multimedia and Expo (ICME), 1–6

  8. Kalayeh MM, Basaran E, Gökmen M, Kamasak ME, Shah M (2018) Human semantic parsing for person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1062–1071

  9. Li S, Yu H, Hu R (2020) Attributes-aided part detection and refinement for person re-identification. Pattern Recogn 97:107016

    Article  Google Scholar 

  10. Lin Y, Zheng L, Zheng Z, Wu Y, Yang Y (2017) Improving person re-identification by attribute and identity learning, in arXiv preprint, arXiv: 1703.07220

  11. 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 95:151–161

    Article  Google Scholar 

  12. Ling H, Wang Z, Li P, Shi Y, Chen J, Zou F (2019) Improving person re-identification by multi-task learning. Neurocomputing 347:109–118

    Article  Google Scholar 

  13. Liu X, Zhao H, Tian M, Sheng L, Shao J, Yi S, Yan J, Wang X (2017) HydraPlus-Net: attentive deep features for pedestrian analysis, in IEEE International Conference on Computer Vision (ICCV), 350–359

  14. Liu L, Zhang Y, Chen J, Gao C (2019) Fusing global and semantic-part features with multiple granularities for person re-identification, in IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, 1436–1440

  15. Luo Y, Zheng Z, Zheng L, Guan T, Yu J, Yang Y (2018) Macro-Micro adversarial network for human parsing, in European Conference on Computer Vision(ECCV), 424–440

  16. Luo J, Liu Y, Gao C, Sang N (2019) Learning what and where from attributes to improve person re-identification, in IEEE International Conference on Image Processing (ICIP), 165–169

  17. Ou X, Ma Q, Wang Y (2019) Improving person re-identification by multi-task learning. Multimed Tools Appl 78(19):28257–28283

  18. Quan H, Feng S, Lang C, Chen B (2020) Improving person re-identification via attribute-identity representation and visual attention mechanism. Multimed Tools Appl 79(11-12):7259–7278

  19. 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 (ECCV), 17–35

  20. Si J, Zhang H, Li C, Kuen J, Kong X, Kot AC, Wang G (2018) Dual attention matching network for context-aware feature sequence based person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5363–5372

  21. Song C, Huang Y, Ouyang W (2018) Mask-guided contrastive attention model for person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1179–1188

  22. Su C, Yang F, Zhang S, Tian Q, Davis LS, Gao W (2018) Multi-task learning with low rank attribute embedding for multi-camera person re-identification. IEEE Trans Pattern Anal Mach Intell 40(5):1167–1180

    Article  Google Scholar 

  23. Su C, Zhang S, Xing J, Gao W, Tian Q (2018) Multi-type attributes driven multi–camera person re-identification. Pattern Recogn 75:77–89

    Article  Google Scholar 

  24. 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 European Conference on Computer Vision (ECCV), 480–496

  25. Tay C, Roy S, Yap K (2019) AANet: attribute attention network for person re-identifications, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7134–7143

  26. Watson G, Bhalerao A (2020) Person re-identification combining deep features and attribute detection. Multimed Tools Appl 79(9-10):6463–6481

  27. Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer GAN to bridge domain gap for person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 79–88

  28. Wei L, Zhang S, Yao H, Gao W, Tian Q (2019) GLAD: global–local-alignment descriptor for scalable person re-identification. IEEE Trans Multimed 21(4):986–999

    Article  Google Scholar 

  29. Xiang J, Lin R, Hou J, Huang W (2018) Person re-identification based on feature fusion and triplet loss function, in IEEE International Conference on Computer Vision (ICCV), 3477–3482

  30. 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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1389–1398

  31. Yin J, Fan Z, Chen S, Wang Y (2020) In-depth exploration of attribute information for person re-identification. Appl Intell 50:3607–3622

    Article  Google Scholar 

  32. Zhao H, Tian M, Sun S (2017) Spindle net: Person re-identification with human body region guided feature decomposition and fusion, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1077–1085

  33. Zhao D, Wand H, Yin H, Yu Z, Li H (2020) Person re-identification by integrating metric learning and support vector machine. Signal Process 166:107277

    Article  Google Scholar 

  34. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark, in IEEE International Conference on Computer Vision (ICCV), 1116–1124

  35. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929

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Acknowledgments

The work was supported in part by the National Nature Science Foundation of China [grant number 61872030] and Major Science and Technology Innovation Project of Shandong Province [grant number 2019TSLH0206].

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Yanfeng Li, Bin Zhang, Jia Sun, Houjin Chen, Xiaodi Zhu & Jinlei Zhu

  2. Synthesis Electronic Technology Co., Ltd, Jinan, China

    Jinlei Zhu

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  1. Yanfeng Li
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  2. Bin Zhang
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  3. Jia Sun
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  4. Houjin Chen
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  5. Xiaodi Zhu
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Correspondence to Yanfeng Li.

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Li, Y., Zhang, B., Sun, J. et al. Person re-identification based on activation guided identity and attribute classification model. Multimed Tools Appl 80, 14961–14977 (2021). https://doi.org/10.1007/s11042-021-10545-4

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  • DOI: https://doi.org/10.1007/s11042-021-10545-4

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