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Person Re-Identification Using Partial Least Squares Appearance Modelling

  • Gregory Watson
  • Abhir Bhalerao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)

Abstract

Person Re-Identification is an important task in surveillance and security systems. Whilst most methods work by extracting features from the entire image, the best methods improve performance by prioritising features from foreground regions during the feature extraction stage. In this paper, we propose the use of a Partial Least Squares Regression model to predict the skeleton of a person, allowing us to prioritise features from a person’s limbs rather than from the background. Once the foreground area has been identified, we use the LOMO [9] and Salient Colour Names [21] features. We then use the XQDA [9] Distance Metric Learning method to compute the distance between each of the feature vectors. Experiments on VIPeR [4], QMUL GRID [12, 13, 14] and CUHK03 [8] data sets demonstrate significant improvements against state-of-the-art.

References

  1. 1.
    Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings the IEEE Conference on CVPR, pp. 3908–3916 (2015)Google Scholar
  2. 2.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Proceeding IEEE Conference on CVPR, pp. 2360–2367, June 2010Google Scholar
  3. 3.
    Forssen, P.E.: Maximally stable colour regions for recognition and matching. In: Proceedings of the IEEE Conference on CVPR, pp. 1–8, June 2007Google Scholar
  4. 4.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3, no. 5, October 2007Google Scholar
  5. 5.
    Jojic, N., Perina, A., Cristani, M., Murino, V., Frey, B.: Stel component analysis: modeling spatial correlations in image class structure. In: IEEE Conference on CVPR, pp. 2044–2051, June 2009Google Scholar
  6. 6.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on CVPR, pp. 2288–2295, June 2012Google Scholar
  7. 7.
    Land, E.H., McCann, J.J.: Lightness and retinex theory. JOSA 61(1), 1–11 (1971)CrossRefGoogle Scholar
  8. 8.
    Li, W., Zhao, R., Xiao, T., Wang, X.: Deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on CVPR, pp. 152–159 (2014)Google Scholar
  9. 9.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on CVPR, pp. 2197–2206 (2015)Google Scholar
  10. 10.
    Liao, S., Li Stan, Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE Conference on ICCV, pp. 3685–3693, December 2015Google Scholar
  11. 11.
    Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on CVPR, pp. 1301–1306, June 2010Google Scholar
  12. 12.
    Liu, C., Gong, S., Loy, C.C., Lin, X.: Person re-identification: what features are important? In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7583, pp. 391–401. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33863-2_39 CrossRefGoogle Scholar
  13. 13.
    Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: Proceedings of the IEEE Conference on CVPR, pp. 1988–1995, June 2009Google Scholar
  14. 14.
    Loy, C.C., Xiang, T., Gong, S.: Time-delayed correlation analysis for multi-camera activity understanding. IJCV 90(1), 106–129 (2010)CrossRefGoogle Scholar
  15. 15.
    Loy, C.C., Liu, C., Gong, S.: Person re-identification by manifold ranking. In: Proceedings of the IEEE Conference on ICIP, pp. 3567–3571, September 2013Google Scholar
  16. 16.
    Maitra, S., Yan, J.: Principle component analysis and partial least squares: two dimension reduction techniques for regression. Appl. Multivar. Stat. Models 79, 79–90 (2008)Google Scholar
  17. 17.
    Rosipal, R., Krämer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34–51. Springer, Heidelberg (2006). doi: 10.1007/11752790_2 CrossRefGoogle Scholar
  18. 18.
    Russakovsky, O., Lin, Y., Yu, K., Fei-Fei, L.: Object-centric spatial pooling for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 1–15. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33709-3_1 Google Scholar
  19. 19.
    Van De Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wang, J., Wang, Z., Gao, C., Sang, N., Huang, R.: DeepList: learning deep features with adaptive listwise constraint for person re-identification. IEEE Trans. Circ. Syst. Video Technol. 27, 513–524 (2017)CrossRefGoogle Scholar
  21. 21.
    Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.Z.: Salient color names for person re-identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 536–551. Springer, Cham (2014). doi: 10.1007/978-3-319-10590-1_35 Google Scholar
  22. 22.
    Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: Proceedings of the IEEE Conference on CVPR, pp. 144–151 (2014)Google Scholar
  23. 23.
    Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: Proceedings of the IEEE Conference on CVPR, pp. 1239–1248 (2016)Google Scholar
  24. 24.
    Zhang, Q., Bhalerao, A., Helm, E., Hutchinson C.: Active shape model unleashed with multi-scale local appearance. In: Proceedings of the IEEE Conference on ICIP, pp. 4664–4668 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WarwickCoventryUK

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