Re-identification by Covariance Descriptors

Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

This chapter addresses the problem of appearance matching, while employing the covariance descriptor. We tackle the extremely challenging case in which the same nonrigid object has to be matched across disjoint camera views. Covariance statistics averaged over a Riemannian manifold are fundamental for designing appearance models invariant to camera changes. We discuss different ways of extracting an object appearance by incorporating various training strategies. Appearance matching is enhanced either by discriminative analysis using images from a single camera or by selecting distinctive features in a covariance metric space employing data from two cameras. By selecting only essential features for a specific class of objects (e.g., humans) without defining a priori feature vector for extracting covariance, we remove redundancy from the covariance descriptor and ensure low computational cost. Using a feature selection technique instead of learning on a manifold, we avoid the over-fitting problem. The proposed models have been successfully applied to the person re-identification task in which a human appearance has to be matched across nonoverlapping cameras. We carry out detailed experiments of the suggested strategies, demonstrating their pros and cons w.r.t. recognition rate and suitability to video analytics systems.

References

  1. 1.
    Bak, S., Charpiat, G., Corvee, E., Bremond, F., Thonnat, M.: Learning to match appearances by correlations in a covariance metric space. In: Proceedings of the 12th European Conference on Computer Vision, IEEE Computer Society (2012)Google Scholar
  2. 2.
    Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Person re-identification using spatial covariance regions of human body parts. In: Proceedings of the 7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE Computer Society (2010)Google Scholar
  3. 3.
    Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Multiple-shot human re-identification by mean riemannian covariance grid. In: Proceedings of the 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS. IEEE Computer Society (2011)Google Scholar
  4. 4.
    Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Boosted human re-identification using riemannian manifolds. Image Vis. Comput. 30(6–7), 443–452 (2012)CrossRefGoogle Scholar
  5. 5.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)Google Scholar
  6. 6.
    Bazzani, L., Cristani, M., Perina, A., Murino, V.: Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn. Lett. 33(7), 898–903 (2012) (Special Issue on Awards from ICPR 2010).Google Scholar
  7. 7.
    Cai, Y., Takala, V., Pietikainen, M.: Matching groups of people by covariance descriptor. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 2744–2747. IEEE Computer Society (2010)Google Scholar
  8. 8.
    Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Proceedings of the 22nd British Machine Vision Conference, pp. 68.1-68.11. BMVA Press (2011)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 18th Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Computer Society (2005)Google Scholar
  10. 10.
    Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N.: Pedestrian recognition with a learned metric. In: Proceedings of the 10th Asian Conference on Computer Vision, pp. 501–512. IEEE Computer Society (2010)Google Scholar
  11. 11.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the International Joint Conference on Uncertainty in AI, IJCAI, pp. 1022–1027 (1993)Google Scholar
  12. 12.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  13. 13.
    Förstner, W., Moonen, B.: A metric for covariance matrices. In: Quo vadis geodesia ...?, Festschrift for Erik W. Grafarend on the occasion of his 60th birthday, TR Deptadtment of Geodesy and Geoinformatics, Stuttgart University (1999)Google Scholar
  14. 14.
    Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proceedings of the 19th Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1528–1535. IEEE Computer Society (2006)Google Scholar
  15. 15.
    Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: Discriminatively trained deformable part models, release 5. http://people.cs.uchicago.edu/rbg/latent-release5/
  16. 16.
    Goh, A., Vidal, R.: Unsupervised riemannian clustering of probability density functions. In: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases—Part I, ECML PKDD, pp. 377–392. Springer, Berlin (2008)Google Scholar
  17. 17.
    Gray, D., Brennan, S., Tao, H.: Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. In: Proceedings of the IEEE International Workshop on Performance Evaluation for Tracking and Surveillance, PETS. IEEE Computer Society (2007)Google Scholar
  18. 18.
    Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. Ph.D. thesis, Department of Computer Science, University of Waikato (1999)Google Scholar
  19. 19.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Proceedings of the 17th Scandinavian Conference on Image Analysis, pp. 91–102. Springer, Berlin (2011)Google Scholar
  20. 20.
    Hordley, S.D., Finlayson, G.D., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognit. 38(2), 179–190 (2005)Google Scholar
  21. 21.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: Proceedings of the 25th Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)Google Scholar
  22. 22.
    Ma, B., Su, Y., Jurie, F.: Bicov: a novel image representation for person re-identification and face verification. In: Proceedings of the 23rd British Machine Vision Conference (2012)Google Scholar
  23. 23.
    Oncel, F.P., Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on lie algebra. In: Proceedings of the 19th Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  24. 24.
    Pennec, X., Fillard, P., Ayache, N.: A riemannian framework for tensor computing. Int. J. Comput. Vis. 66(1), 41–66 (2006)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Rich, E., Knight, K.: Artificial Intelligence. McGraw-Hill Higher Education (1991)Google Scholar
  26. 26.
    Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Proceedings of the 9th European Conference on Computer Vision, pp. 589–600. Springer (2006)Google Scholar
  27. 27.
    Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1713–1727 (2008)Google Scholar
  28. 28.
    Yao, J., Odobez, J.M.: Fast human detection from joint appearance and foreground feature subset covariances. Comput. Vis. Image Underst. 115(3), 1414–1426 (2011)CrossRefGoogle Scholar
  29. 29.
    Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: Proceedings of the 20th British Machine Vision Conference, BMVC Press (2009)Google Scholar
  30. 30.
    Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Proceedings of the 24th Conference on Computer Vision and Pattern Recognition, pp. 649–656. IEEE Computer Society (2011)Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.INRIASophia AntipolisFrance

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