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Ranking Loss: A Novel Metric Learning Method for Person Re-identification

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11362))

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Abstract

Person re-identification is the problem of matching pedestrians under different camera views. The goal of person re-identification is to make the truly matched pedestrian pair rank as the first place among all pairs, with the direct translation in math language, which equals that the distance of matched pedestrian pair is the minimum value of the distances of all pairs. In this paper, we propose a novel metric learning method for person re-identification to learn such an optimal feature mapping function, which minimizes the difference between the distance of matched pair and the minimum distance of all pairs, namely Ranking Loss. Furthermore, we develop an improved version of ranking loss by using p-norm as a smooth approximation of minimum function, with the advantage of manipulating parameter p to control the distance margin between matched pair and unmatched pair to benefit the re-identification accuracy. We also present an efficient solver using only a small portion of pairs in computation, achieving almost the same performance as using all. Compared with other loss function, the proposed ranking loss optimizes the ultimate ranking goal in the most direct and intuitional way, and it directly acts on the whole gallery set efficiently instead of comparatively measuring in small subset. The detailed theoretical discussion and experimental comparisons with other loss functions are provided, illustrating the advantages of the proposed ranking loss. Extensive experiments on two datasets also show the effectiveness of the proposed method compared to state-of-the-art methods.

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References

  1. Zhao, Z., Zhao, B., Su, F.: Person re-identification via integrating patch-based metric learning and local salience learning. Pattern Recogn. 75, 90–98 (2017)

    Article  Google Scholar 

  2. Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Computer Vision and Pattern Recognition, vol. 2 (2017)

    Google Scholar 

  3. Sun, C., Wang, D., Lu, H.: Person re-identification via distance metric learning with latent variables. IEEE Trans. Image Process. 26(1), 23–34 (2016)

    Article  MathSciNet  Google Scholar 

  4. Zhou, S., Wang, J., Shi, R., Hou, Q., Gong, Y., Zheng, N.: Large margin learning in set to set similarity comparison for person re-identification. IEEE Trans. Multimed. PP(99), 1–1 (2017)

    Article  Google Scholar 

  5. Jurie, F., Mignon, A.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Computer Vision and Pattern Recognition, pp. 2666–2672 (2012)

    Google Scholar 

  6. Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3610–3617 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  8. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Computer Vision and Pattern Recognition, pp. 2360–2367 (2010)

    Google Scholar 

  9. Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)

    Google Scholar 

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

    Google Scholar 

  11. Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: Computer Vision and Pattern Recognition, pp. 3318–3325 (2013)

    Google Scholar 

  12. Cao, M., Chen, C., Hu, X., Peng, S.: From groups to co-traveler sets: pair matching based person re-identification framework. In: IEEE International Conference on Computer Vision Workshop, pp. 2573–2582 (2017)

    Google Scholar 

  13. Chen, C., Cao, M., Hu, X., Peng, S.: Key person aided re-identification in partially ordered pedestrian set. In: Conference the British Machine Vision Conference (2017)

    Google Scholar 

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

    Google Scholar 

  15. Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., Radke, R.J.: A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets. IEEE Trans. Pattern Anal. Mach. Intell., 1 (2016)

    Google Scholar 

  16. Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1239–1248 (2016)

    Google Scholar 

  17. Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L.: Joint learning of single-image and cross-image representations for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1288–1296 (2016)

    Google Scholar 

  18. Zhou, S., Wang, J., Wang, J., Gong, Y., Zheng, N.: Point to set similarity based deep feature learning for person re-identification. In: Computer Vision and Pattern Recognition, pp. 5028–5037 (2017)

    Google Scholar 

  19. Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31

    Chapter  Google Scholar 

  20. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS) (2007)

    Google Scholar 

  21. Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 31–44. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_3

    Chapter  Google Scholar 

  22. Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363–1372 (2016)

    Google Scholar 

  23. Chen, Y.-C., Zhu, X., Zheng, W.-S., Lai, J.-H.: Person re-identification by camera correlation aware feature augmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 392–408 (2018)

    Article  Google Scholar 

  24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  25. Tian, M., et al.: Eliminating background-bias for robust person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018

    Google Scholar 

  26. Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)

    Google Scholar 

  27. Chen, S.Z., Guo, C.C., Lai, J.: Deep ranking for person re-identification via joint representation learning. IEEE Trans. Image Process. 25(5), 2353–2367 (2016)

    Article  MathSciNet  Google Scholar 

  28. Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)

    Google Scholar 

  29. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: The IEEE International Conference on Computer Vision (ICCV), October 2017

    Google Scholar 

  30. Zhao, L., Li, X., Zhuang, Y., Wang, J.: Deeply-learned part-aligned representations for person re-identification. In: The IEEE International Conference on Computer Vision (ICCV), October 2017

    Google Scholar 

  31. Guo, Y., Cheung, N.-M.: Efficient and deep person re-identification using multi-level similarity. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018

    Google Scholar 

  32. Mao, C., Li, Y., Zhang, Y., Zhang, Z., Li, X.: Multi-channel pyramid person matching network for person re-identification (2018)

    Google Scholar 

  33. Jose, C., Fleuret, F.: Scalable metric learning via weighted approximate rank component analysis. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 875–890. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_53

    Chapter  Google Scholar 

  34. Martinel, N., Das, A., Micheloni, C., Roy-Chowdhury, A.K.: Temporal model adaptation for person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 858–877. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_52

    Chapter  Google Scholar 

  35. Yang, X., Wang, M., Tao, D.: Person re-identification with metric learning using privileged information. IEEE Trans. Image Process. PP(99), 1 (2018)

    Google Scholar 

  36. Zhou, Q., et al.: Graph correspondence transfer for person re-identification (2018)

    Google Scholar 

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Acknowledgment

This work is supported by the National Key R&D Program of China under Grant 2017YFC0803505. We appreciate Hao Dou’s help on this paper.

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Authors and Affiliations

  1. Institute of Automation Chinese Academy of Sciences, Beijing, China

    Min Cao, Chen Chen, Xiyuan Hu & Silong Peng

  2. Beijing Visytem Co., Ltd., Beijing, China

    Silong Peng

  3. University of Chinese Academy of Sciences, Beijing, China

    Min Cao, Chen Chen, Xiyuan Hu & Silong Peng

Authors
  1. Min Cao
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  2. Chen Chen
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  3. Xiyuan Hu
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  4. Silong Peng
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Corresponding author

Correspondence to Chen Chen .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Cao, M., Chen, C., Hu, X., Peng, S. (2019). Ranking Loss: A Novel Metric Learning Method for Person Re-identification. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_25

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  • DOI: https://doi.org/10.1007/978-3-030-20890-5_25

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  • Online ISBN: 978-3-030-20890-5

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