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Learning Implicit Transfer for Person Re-identification

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7583)

Abstract

This paper proposes a novel approach for pedestrian re-identification. Previous re-identification methods use one of 3 approaches: invariant features; designing metrics that aim to bring instances of shared identities close to one another and instances of different identities far from one another; or learning a transformation from the appearance in one domain to the other. Our implicit approach models camera transfer by a binary relation R = {(x,y)|x and y describe the same person seen from cameras A and B respectively}. This solution implies that the camera transfer function is a multi-valued mapping and not a single-valued transformation, and does not assume the existence of a metric with desirable properties. We present an algorithm that follows this approach and achieves new state-of-the-art performance.

Keywords

  • Binary Relation
  • True Match
  • Positive Pair
  • Negative Pair
  • Cumulative Match Characteristic

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: PETS Workshop in Conjunction with ICCV (2007)

    Google Scholar 

  2. Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: BMVC (2011)

    Google Scholar 

  3. Pinto, N., DiCarlo, J., Cox, D.: How far can you get with a modern face recognition test set using only simple features? In: CVPR (2009)

    Google Scholar 

  4. Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: ECCV (2008)

    Google Scholar 

  5. Ferencz, A., Learned-miller, E., Malik, J.: Learning to locate informative features for visual identification. IJCV 77, 3–24 (2008)

    CrossRef  Google Scholar 

  6. Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: CVPR (2006)

    Google Scholar 

  7. Hu, W., Hu, M., Zhou, X., Tan, T., Lou, J.: Principal axis-based correspondence between multiple cameras for people tracking. PAMI 28, 663–671 (2006)

    CrossRef  Google Scholar 

  8. Cong, D., Khoudour, L., Achard, C., Meurie, C., Lezoray, O.: People re-identification by spectral classification of silhouettes. Signal Processing 90 (2010)

    Google Scholar 

  9. Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-shot person re-identification by HPE signature. In: ICPR, pp. 1413–1416 (2010)

    Google Scholar 

  10. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: CVPR (2010)

    Google Scholar 

  11. 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 

  12. Prosser, B., Zheng, W., Shaogang, G., Xiang, T.: Person re-identification by support vector ranking. In: BMVC (2010)

    Google Scholar 

  13. Zheng, W., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR (2011)

    Google Scholar 

  14. Javed, O., Khurram, S., Mubarak, S.: Appearance modeling for tracking in multiple non-overlapping cameras. In: CVPR (2005)

    Google Scholar 

  15. Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: BMVC (2009)

    Google Scholar 

  16. Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2001), Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm

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Avraham, T., Gurvich, I., Lindenbaum, M., Markovitch, S. (2012). Learning Implicit Transfer for Person Re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_38

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  • DOI: https://doi.org/10.1007/978-3-642-33863-2_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33862-5

  • Online ISBN: 978-3-642-33863-2

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