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Fast and Accurate Person Re-identification with Xception Conv-Net and C2F

  • Arthur van RooijenEmail author
  • Henri Bouma
  • Fons Verbeek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Person re-identification (re-id) is the task of identifying a person of interest across disjoint camera views in a multi-camera system. This is a challenging problem due to the different poses, viewpoints and lighting conditions. Deeply learned systems have become prevalent in the person re-identification field as they are capable to deal with the these obstacles. Conv-Net using a coarse-to-fine search framework (Conv-Net+C2F) is such a deeply learned system, which has been developed with both a high-retrieval accuracy as a fast query time in mind. We propose three contributions to improve Conv-Net+C2F: (1) training with an improved optimizer, (2) constructing Conv-Net using a different Convolutional Neural Network (CNN) not yet used for person re-id and (3) coarse descriptors having fewer dimensions for improved speed as well as increased accuracy. With these adaptations Xception Conv-Net+C2F achieves state-of-the-art results on Market-1501 (single-query, 72.4% mAP) and the new, challenging data split of CUHK03 (detected, 42.6% mAP).

Keywords

Person re-identification Large-scale person retrieval Convolutional neural networks Image retrieval Feature extraction 

References

  1. 1.
    Chollet, F., et al.: Keras (2017). https://github.com/fchollet/keras
  2. 2.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv:1610.02357 (2016)
  3. 3.
    Dozat, T.: Incorporating Nesterov momentum into Adam. openreview (2016)Google Scholar
  4. 4.
    Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12, 2121–2159 (2011)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person re-identification. arXiv:1611.05244 (2016)
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE CVPR, pp. 770–778 (2016)Google Scholar
  7. 7.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv:1703.07737 (2017)
  8. 8.
    Howard, A., Zhu, M., Chen, B., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)
  9. 9.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv (2014)Google Scholar
  10. 10.
    Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: IEEE CVPR, pp. 152–159 (2014)Google Scholar
  11. 11.
    Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. arXiv:1705.04724 (2017)
  12. 12.
    Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. arXiv:1703.07220 (2017)
  13. 13.
    Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400–407 (1951)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  16. 16.
    Sun, Y., et al.: SVDNet for pedestrian retrieval. arXiv (2017)Google Scholar
  17. 17.
    Szegedy, C., et al.: Going deeper with convolutions. In: IEEE CVPR (2015)Google Scholar
  18. 18.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arxiv:1602.07261
  19. 19.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE CVPR, pp. 2818–2826 (2016)Google Scholar
  20. 20.
    Tieleman, T., Hinton, G.: RMSprop Gradient Optimization. Neural Networks for Machine Learning (2015). http://www.cs.toronto.edu/
  21. 21.
    Yao, H., et al.: Large-scale person re-identification as retrieval. In: ICME (2017)Google Scholar
  22. 22.
    Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv:1212.5701 (2012)
  23. 23.
    Zheng, L., Shen, L., Tian, L., et al.: Scalable person re-identification: A benchmark. In: IEEE ICCV, pp. 1116–1124 (2015)Google Scholar
  24. 24.
    Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: ICCV (2017)Google Scholar
  25. 25.
    Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. arXiv:1701.08398 (2017)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Arthur van Rooijen
    • 1
    • 2
    Email author
  • Henri Bouma
    • 1
  • Fons Verbeek
    • 2
  1. 1.TNOThe HagueNetherlands
  2. 2.Leiden UniversityLeidenNetherlands

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