Relaxed Pairwise Learned Metric for Person Re-identification

  • Martin Hirzer
  • Peter M. Roth
  • Martin Köstinger
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs.

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References

  1. 1.
    Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proc. CVPR (2006)Google Scholar
  2. 2.
    Wang, X., Doretto, G., Sebastian, T.B., Rittscher, J., Tu, P.H.: Shape and appearance context modeling. In: Proc. ICCV (2007)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Bak, S., Corvee, E., Brémond, F., Thonnat, M.: Person re-idendification using Haar-based and DCD-based signature. In: Proc. Workshop on Activity Monitoring by Multi-Camera Surveillance Systems (2010)Google Scholar
  5. 5.
    Lin, Z., Davis, L.S.: Learning pairwise dissimilarity profiles for appearance recognition in visual surveillance. In: Advances Int’l Visual Computing Symposium (2008)Google Scholar
  6. 6.
    Schwartz, W.R., Davis, L.S.: Learning discriminative appearance-based models using partial least squares. In: Proc. Brazilian Symposium on Computer Graphics and Image Processing (2009)Google Scholar
  7. 7.
    Prosser, B., Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: Proc. BMVC (2010)Google Scholar
  8. 8.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person Re-identification by Descriptive and Discriminative Classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: Proc. BMVC (2009)Google Scholar
  10. 10.
    Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N.: Pedestrian Recognition with a Learned Metric. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 501–512. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proc. PETS (2007)Google Scholar
  12. 12.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Proc. CVPR (2010)Google Scholar
  13. 13.
    Rahimi, A., Dunagan, B., Darrell, T.: Simultaneous calibration and tracking with a network of non-overlapping sensors. In: Proc. CVPR (2004)Google Scholar
  14. 14.
    Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Information Retrieval 13, 201–215 (2010)CrossRefGoogle Scholar
  15. 15.
    Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Proc. CVPR (2011)Google Scholar
  16. 16.
    Weinberger, K.Q., Saul, L.K.: Fast solvers and efficient implementations for distance metric learning. In: Proc. ICML (2008)Google Scholar
  17. 17.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proc. ICML (2007)Google Scholar
  18. 18.
    Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: Proc. ICCV (2009)Google Scholar
  19. 19.
    Ghodsi, A., Wilkinson, D.F., Southey, F.: Improving embeddings by flexible exploitation of side information. In: Proc. Int’l Joint Conf. on Artificial Intelligence (2007)Google Scholar
  20. 20.
    Alipanahi, B., Biggs, M., Ghodsi, A.: Distance metric learning vs. fisher discriminant analysis. In: Proc. AAAI Conf. on Artificial Intelligence (2008)Google Scholar
  21. 21.
    Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Proc. BMVC (2011)Google Scholar
  22. 22.
    Baltieri, D., Vezzani, R., Cucchiara, R.: SARC3D: A New 3D Body Model for People Tracking and Re-identification. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part I. LNCS, vol. 6978, pp. 197–206. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: Proc. CVPR (2004)Google Scholar
  24. 24.
    Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: Proc. CVPR (2005)Google Scholar
  25. 25.
    Ess, A., Leibe, B., Van Gool, L.: Depth and appearance for mobile scene analysis. In: Proc. ICCV (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martin Hirzer
    • 1
  • Peter M. Roth
    • 1
  • Martin Köstinger
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria

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