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Global-Local Mutual Guided Learning for Person Re-identification

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12306))

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Abstract

Person Re-Identification (Re-ID) plays a significant role in intelligent surveillance systems. Existing popular methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, where pedestrian part-level features are inefficient to fully utilize the global feature information. Besides that, some methods miss out semantic transition information of human body. In this paper, we propose an end-to-end feature learning strategy to get refined feature representations with global-local mutual guided learning. In order to explore global and local information, we design a Global-Local Mutual Guided Network (GLMG-Net). It contains two branches to learn global feature representations, and local feature representations, respectively. For mutual guided module, global features are combined with each local feature by the add-wise operation. In the training process, this module enables branches to guide each other. Comprehensive experiments conducted on the public datasets of Market-1501 and DukeMTMC-ReID indicate that our method outperforms state-of-the-art approaches in several cases. In particular, mean average precision (mAP) scores of our method on those benchmarks are 89.2% and 79.7%, respectively.

J. Chen—The postgraduate student.

Supported by the National Natural Science Foundation of China (No. 61801068, 61502067, 61972060, U1713213), and Natural Science Foundation of Chongqing (cstc2015jcyjA40013, cstc2015jcyjA40034).

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References

  1. Ahmed, E., Jones, M.J., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)

    Google Scholar 

  2. Almazán, J., Gajic, B., Murray, N., Larlus, D.: Re-id done right: towards good practices for person re-identification. CoRR (2018)

    Google Scholar 

  3. Bai, X., Yang, M., Huang, T., Dou, Z., Yu, R., Xu, Y.: Deep-person: learning discriminative deep features for person re-identification. Pattern Recogn. 98, 107036 (2020)

    Article  Google Scholar 

  4. Chang, X., Hospedales, T.M., Xiang, T.: Multi-level factorisation net for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2109–2118 (2018)

    Google Scholar 

  5. Chen, D., Xu, D., Li, H., Sebe, N., Wang, X.: Group consistent similarity learning via deep CRF for person re-identification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8649–8658 (2018)

    Google Scholar 

  6. Chen, W., Chen, X., Zhang, J., Huang, K.: A multi-task deep network for person re-identification. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 3988–3994 (2017)

    Google Scholar 

  7. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  9. Felzenszwalb, P.F., McAllester, D.A., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 26th IEEE Conference on Computer Society Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  10. Fu, Y., et al.: Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8295–8302 (2019)

    Google Scholar 

  11. Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448–456 (2015)

    Google Scholar 

  12. Li, H., Yang, M., Lai, Z., Zheng, W., Yu, Z.: Pedestrian re-identification based on tree branch network with local and global learning. In: Proceedings - IEEE International Conference on Multimedia and Expo, pp. 694–699 (2019)

    Google Scholar 

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

    Google Scholar 

  14. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  15. Shen, Y., et al.: A part power set model for scale-free person retrieval. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 3397–3403 (2019)

    Google Scholar 

  16. Si, J., et al.: Dual attention matching network for context-aware feature sequence based person re-identification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5363–5372 (2018)

    Google Scholar 

  17. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3980–3989 (2017)

    Google Scholar 

  18. Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30

    Chapter  Google Scholar 

  19. Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 2018 ACM Multimedia Conference, pp. 274–282 (2018)

    Google Scholar 

  20. Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: GLAD: global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 420–428 (2017)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  23. Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3774–3782 (2017)

    Google Scholar 

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

    Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Junheng Chen, Xiao Luan & Weisheng Li

Authors
  1. Junheng Chen
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  2. Xiao Luan
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  3. Weisheng Li
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Corresponding author

Correspondence to Junheng Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Chen, J., Luan, X., Li, W. (2020). Global-Local Mutual Guided Learning for Person Re-identification. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-60639-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60638-1

  • Online ISBN: 978-3-030-60639-8

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