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Coupled-View Based Ranking Optimization for Person Re-identification

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8935)

Abstract

Person re-identification aims to match different persons observed in non-overlapping camera views. Researchers have proposed many person descriptors based on global or local descriptions, while both of them have achieved satisfying matching results, however, their ranking lists usually vary a lot for the same query person. These motivate us to investigate an approach to aggregate them to optimize the original matching results. In this paper, we proposed a coupled-view based ranking optimization method through cross KNN rank aggregation and graph-based re-ranking to revise the original ranking lists. Its core assumption is that the images of the same person should share the similar visual appearance in both global and local views. Extensive experiments on two datasets show the superiority of our proposed method with an average improvement of 20-30% over the state-of-the-art methods at CMC@1.

Keywords

  • Coupled-view
  • Ranking optimization
  • Person re-identification

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  • DOI: 10.1007/978-3-319-14445-0_10
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References

  1. Gheissari, N., Sebastian, T.B., et al.: Person re-identification using spatiotemporal appearance. In: Computer Vision and Pattern Recognition (CVPR), pp. 1528–1535 (2006)

    Google Scholar 

  2. Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

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

    Google Scholar 

  4. Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Computer Vision and Pattern Recognition (CVPR), pp. 649–656 (2011)

    Google Scholar 

  5. Kostinger, M., Hirzer, M., Wohlhart, P., et al.: Large scale metric learning from equivalence constraints. In: Computer Vision and Pattern Recognition (CVPR), pp. 2288–2295 (2012)

    Google Scholar 

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

    Google Scholar 

  7. Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: Computer Vision and Pattern Recognition (CVPR), pp. 3586–3593 (2013)

    Google Scholar 

  8. Xu, Y., Lin, L., Zheng, W.S., et al.: Human re-identification by matching compositional template with cluster sampling. In: International Conference on Computer Vision (ICCV), pp. 3152–3159 (2013)

    Google Scholar 

  9. Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 26–33 (2005)

    Google Scholar 

  10. Wang, X., Doretto, G., Sebastian, T., et al.: Shape and appearance context modeling. In: International Conference on Computer Vision (ICCV), pp. 1–8 (2007)

    Google Scholar 

  11. Hirzer, M., Roth, P.M., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 780–793. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  12. Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: European Conference on Computer Vision Workshops and Demonstrations (ECCV Workshop), pp. 413–422 (2012)

    Google Scholar 

  13. Leng, Q., Hu, R., Liang, C., et al.: Bidirectional ranking for person re-identification. In: International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)

    Google Scholar 

  14. Liu, C., Loy, C.C., Gong, S., et al.: POP: Person re-identification post-rank optimisation. In: International Conference on Computer Vision (ICCV), pp. 441–448 (2013)

    Google Scholar 

  15. An, L., Chen, X., Kafai, M., et al.: Improving person re-identification by soft biometrics based reranking. In: International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (2013)

    Google Scholar 

  16. Leng, Q., Hu, R., Liang, C., et al.: Person re-identification with content and context re-ranking. In: Multimedia Tools and Applications, pp. 1–26 (2014)

    Google Scholar 

  17. Ali, S., Javed, O., Haering, N., et al.: Interactive retrieval of targets for wide area surveillance. In: International Conference on Multimedia (MM), pp. 895–898 (2010)

    Google Scholar 

  18. Hirzer, M., Beleznai, C., Roth, P.M., et al.: Person re-identification by descriptive and discriminative classification. In: Image Analysis (IA), pp. 91–102 (2011)

    Google Scholar 

  19. Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: International Conference on Computer Vision (ICCV), pp. 2528–2535 (2013)

    Google Scholar 

  20. Zhang, S., Yang, M., Cour, T., et al.: Query specific fusion for image retrieval. In: European Conference on Computer Vision(ECCV), pp. 660–673 (2012)

    Google Scholar 

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

    Google Scholar 

  22. Li, W., Wang, X.: Locally aligned feature transforms across views. In: Computer Vision and Pattern Recognition (CVPR), pp. 3594–3601 (2013)

    Google Scholar 

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Ye, M., Chen, J., Leng, Q., Liang, C., Wang, Z., Sun, K. (2015). Coupled-View Based Ranking Optimization for Person Re-identification. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-14445-0_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14444-3

  • Online ISBN: 978-3-319-14445-0

  • eBook Packages: Computer ScienceComputer Science (R0)