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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)

Abstract

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.

Keywords

Feature Selection Image Pair Probe Image Discriminative Model Covariance Feature 
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. 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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