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Research on person re-identification based on posture guidance and feature alignment

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Abstract

This paper proposes a person re-identification algorithm utilizing posture guidance and feature alignment to solve posture difference and misalignment of the retrieved pedestrian images. Our technique employs Openpose to locate 18 key points on the human body and integrates 18 heat maps of various human body key points into a global feature representation. Then, a hard attention mechanism based on the human body key points forces the network to focus on the pedestrian posture features to align the same body parts of pedestrian imagery. Our architecture solves the pedestrian image posture difference and misalignment problem and performs robust person re-identification. We challenge the developed method on the public Market1501 and DukeMTMC-reID datasets, employing the Rank-1 and mAP performance metrics, and obtain 94.6%/81.4% and 85.7%/72.7%, respectively. The results highlight that the proposed algorithm solves the problems of pedestrian image misalignment and posture difference, proving the effectiveness and practicability of the proposed algorithm.

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The datasets are open and available.

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The code is not currently public.

References

  1. Liao, S., Hu, Y., Zhu, X. et al.: Person re-identification by local maximal occurrence representation nd metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)

  2. Qi, L., Yu, P.Z., Gao, Y.: Review of research on person re-identification under weak supervision. J. Softw. 2020, 9 (2020)

    Google Scholar 

  3. Xu, L., Zhao, H.T., Sun, S.Y.: Monocular infrared imaged depth estimation based on deep convolutional neural network. Acta Opta Sin. 36(07), 196–205 (2016)

    Google Scholar 

  4. Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person re-identification. ACM Trans. Multimedia Comput. Commun. Appl. 14(1), 3159171 (2016)

    MathSciNet  Google Scholar 

  5. Zhao, H., Tian, M., Sun, S. et al.: 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 (2017)

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

  7. Suh, Y., Wang, J., Tang, S., et al.: Part-aligned bilinear representations for person re-identification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 402–419 (2018)

  8. Zheng, Y., Zhao, J.Y., Wang, C., et al.: Local pedestrian re-recognition based on attitude-guided alignment network. Comput. Eng. 46(05), 247–253 (2020)

    Google Scholar 

  9. Zheng, W.S., Li, X., Xiang, T., et al.: Partial person re-identification. Proc. IEEE Int. Conf. Comput. Vis. 2015, 4678–4686 (2015)

    Google Scholar 

  10. Sun, Y., Liang, Z., Yi, Y. et al.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). arXiv: 2018.17110 (2022)

  11. Zhang, X., Luo, H., Fan, X., et al.: AlignedReID: surpassing human-level performance in person re-identification. arXiv: 2017.17110 (2017)

  12. Ku, H.H., Zhou, P., Cai, X.D., et al.: Pedestrian re-recognition method based on regional feature alignment and k-inverted coding. Comput. Eng. 45(03), 207–211 (2019)

    Google Scholar 

  13. Li, Z., Jin, Y., Li, Y., et al.: Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method. World Wide Web 23, 9 (2019)

    Google Scholar 

  14. Zhou, Q., Zhong, B., Lan, X., et al.: LRDNN: Local-refining based deep neural network for person re-identification with attribute discerning. In: Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19 (2019)

  15. Zhou, Q., Zhong, B., Lan, X., et al.: Fine-grained spatial alignment model for person re-identification with focal triplet loss. IEEE Trans. Image Process. 29, 1–1 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  16. Tang, Y.X.: Research on pedestrian re-identification integrating global features and local features. Hefei Univ. Technol. 2021:5 (2021). https://doi.org/10.27101/d.cnki.ghfgu.2021.000319

  17. Wang, P., Zhao, Z., Su, F., et al.: HOReID: deep high-order mapping enhances pose alignment for person re-identification. IEEE Trans. Image Process. 2021, 99 (2021)

    MathSciNet  Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556 (2014)

  19. Alejandro, N., Yang, K.Y., Deng, J.: Stacked hour-glass networks for human pose estimation. IN: InECCV (2016)

  20. Zhang, L., Shen, L.Y., Tian, L., et al.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision. Washington D. C., USA: IEEE Press, pp. 1116–1124 (2015)

  21. Lin, L., Wang, X., Yang, W., et al.: Discriminatively trained and-or graph models for object shape detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(5), 959–972 (2015)

    Article  Google Scholar 

  22. Ristani, E., Solera, F., Zou, R.S., et al.: Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision. Springer, Cham (2016)

  23. Liao, S., Hu, Y., Zhu, X., et al.: Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)

  24. Sun, Y., Zheng, L., Deng, W., et al.: SVDNet for pedestrian retrieval. IEEE Int. Conf. Comput. Vis. 410, 3820–3828 (2017)

    Google Scholar 

  25. Lin, Y.T., Zheng, L., Zheng, Z.D., et al.: Improving Rerson Re-identification by Attribute and Identity Learning. arXiv: 2017,1703.07220 (2021)

  26. Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: IEEE International Conference on Computer Vision (ICCV), Venice, vol. 405, pp. 3374–3782 (2017)

  27. Liu, J., Ni, B., Yan, Y., et al.: Pose transferrable person re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, vol. 2018.00431, pp. 4099–4108 (2018)

  28. Sarfraz, M.S., Schumann. A., Eberle, A., et al.: 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 (2018)

  29. Chen, Y., Zhu, X., Gong, S., et al.: Person re-identification by deep learningmulti-scale representations. In: IEEE International Conference on Computer Vision Workshops (ICCVW), Venice: IEEE, vol. 304, pp. 2590–2600 (2017)

  30. Qian, X., Fu, Y., Xiang, T., et al.: Pose-normalized image generation for person re-identification. arXiv:2017.17120 (2021)

  31. Chang, X., Hospedales, T.M., Xiang, T.: Multi-level Factorisation Net for Person Re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, vol. 00225, pp. 2109–2118 (2018)

  32. Cai, H.L., Wang, Z.G., Cheng, J.X.: Multi-scale body-part mask guided attention for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

  33. Si, J., Zhang, H., Li, C.G., et al.: Dual attention matching network for context-aware feature sequence based person re-identification. In: Proceedings of the IEEE Computer Vision and Pattern Recognition, pp. 5363–5372 (2018)

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Funding

This work was supported by National Natural Science Foundation of China (No. 61861037) and the Ningxia University Graduate Innovation Research Project (No.CXXM202223).

Author information

Author notes

  1. Jin Che, Yuxia Zhang, Qi Yang and Yuting He have contributed equally to this work.

Authors and Affiliations

  1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Wen Cui Street, Yinchuan, 750021, Ningxia, China

    Jin Che, Yuxia Zhang, Qi Yang & Yuting He

  2. Ningxia Key Laboratory of Intelligent Sensing for Desert Information, Ningxia University, Helan Mountain West Road Street, Yinchuan, 750021, Ningxia, China

    Jin Che, Yuxia Zhang, Qi Yang & Yuting He

Authors
  1. Jin Che
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  2. Yuxia Zhang
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  3. Qi Yang
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Contributions

JC: theoretical analysis, experimental methods, experimental ideas, review, and modify the first draft; YZ: preliminary experiment, experimental design, and draft writing; QY: further improved the experiment, conducted data analysis, and wrote the first draft; YH: data collation, draft writing, and editing.

Corresponding author

Correspondence to Jin Che.

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Communicated by I.Bartolini.

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Che, J., Zhang, Y., Yang, Q. et al. Research on person re-identification based on posture guidance and feature alignment. Multimedia Systems 29, 763–770 (2023). https://doi.org/10.1007/s00530-022-01016-3

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  • DOI: https://doi.org/10.1007/s00530-022-01016-3

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