Person Re-identification by Descriptive and Discriminative Classification

  • Martin Hirzer
  • Csaba Beleznai
  • Peter M. Roth
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset.


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  1. 1.
    Bak, S., Corvee, E., Brémond, F., Thonnat, M.: Person re-idendification using Haar-based and DCD-based signature. In: Workshop on Activity Monitoring by Multi-Camera Surveillance Systems (2010)Google Scholar
  2. 2.
    Bird, N.D., Masoud, O., Papanikolopoulos, N.P., Isaacs, A.: Detection of loitering individuals in public transportation areas. IEEE Trans. Intelligent Transportation Systems 6(2), 167–177 (2005)CrossRefGoogle Scholar
  3. 3.
    Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Information Retrieval 13(3), 201–215 (2010)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Förstner, W., Moonen, B.: A metric for covariance matrices. Technical report, Department of Geodesy and Geoinformatics, Stuttgart University (1999)Google Scholar
  6. 6.
    Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proc. CVPR (2006)Google Scholar
  7. 7.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proc. PETS (2007)Google Scholar
  8. 8.
    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
  9. 9.
    Hu, M., Lou, J., Hu, W., Tan, T.: Multicamera correspondence based on principal axis of human body. In: Proc. ICIP (2004)Google Scholar
  10. 10.
    Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: Proc. CVPR (2005)Google Scholar
  11. 11.
    Kluckner, S., Mauthner, T., Roth, P.M., Bischof, H.: Semantic classification in aerial imagery by integrating appearance and height information. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 477–488. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Levi, K., Weiss, Y.: Learning object detection from a small number of examples: The importance of good features. In: Proc. CVPR (2004)Google Scholar
  13. 13.
    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
  14. 14.
    Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: Proc. CVPR (2004)Google Scholar
  15. 15.
    Opelt, A., Axel Pinz, A.Z.: A boundary-fragment-model for object detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Prosser, B., Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: Proc. BMVC (2010)Google Scholar
  17. 17.
    Rahimi, A., Dunagan, B., Darrell, T.: Simultaneous calibration and tracking with a network of non-overlapping sensors. In: Proc. CVPR (2004)Google Scholar
  18. 18.
    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
  19. 19.
    Tieu, K., Viola, P.: Boosting image retrieval. In: Proc. CVPR (2000)Google Scholar
  20. 20.
    Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR (2001)Google Scholar
  22. 22.
    Wang, X., Doretto, G., Sebastian, T.B., Rittscher, J., Tu, P.H.: Shape and appearance context modeling. In: Proc. ICCV (2007)Google Scholar
  23. 23.
    Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: Proc. BMVC (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Martin Hirzer
    • 1
  • Csaba Beleznai
    • 2
  • Peter M. Roth
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria
  2. 2.Austrian Institute of TechnologyAustria

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