Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras

  • Dung Nghi Truong Cong
  • Catherine Achard
  • Louahdi Khoudour
  • Lounis Douadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

This paper presents a solution of the appearance-based people re-identification problem in a surveillance system including multiple cameras with different fields of vision. We first utilize different color-based features, combined with several illuminant invariant normalizations in order to characterize the silhouettes in static frames. A graph-based approach which is capable of learning the global structure of the manifold and preserving the properties of the original data in a lower dimensional representation is then introduced to reduce the effective working space and to realize the comparison of the video sequences. The global system was tested on a real data set collected by two cameras installed on board a train. The experimental results show that the combination of color-based features, invariant normalization procedures and the graph-based approach leads to very satisfactory results.

Keywords

Dimensionality Reduction Video Sequence Multiple Camera Invariant Normalization Nonlinear Dimensionality Reduction 
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.
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References

  1. 1.
    Kettnaker, V., Zabih, R.: Bayesian multi-camera surveillance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (1999)Google Scholar
  2. 2.
    Nakajima, C., Pontil, M., Heisele, M., Poggio, T.: Full body person recognition system. Pattern Recognition 36(9), 1997–2006 (2003)CrossRefMATHGoogle Scholar
  3. 3.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. In: Ninth IEEE International Conference on Computer Vision (2003)Google Scholar
  4. 4.
    Bird, N., Masoud, O., Papanikolopoulos, N., Isaacs, A.: Detection of loitering individuals in public transportation areas. IEEE Transactions on Intelligent Transportation Systems 6(2), 167–177 (2005)CrossRefGoogle Scholar
  5. 5.
    Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 1528–1535. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  6. 6.
    Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (2007)Google Scholar
  7. 7.
    Yu, Y., Harwood, D., Yoon, K., Davis, L.: Human appearance modeling for matching across video sequences. Machine Vision and Applications 18(3), 139–149 (2007)CrossRefMATHGoogle Scholar
  8. 8.
    Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing, ICIP 2004, vol. 5 (2004)Google Scholar
  9. 9.
    Birchfield, S., Rangarajan, S.: Spatiograms versus histograms for region-based tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1158–1163 (2005)Google Scholar
  10. 10.
    Gevers, T., Stokman, H.: Robust histogram construction from color invariants for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 113–118 (2004)Google Scholar
  11. 11.
    Finlayson, G., Hordley, S., Schaefer, G., Yun Tian, G.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38(2), 179–190 (2005)CrossRefGoogle Scholar
  12. 12.
    Madden, C., Piccardi, M., Zuffi, S.: Comparison of Techniques for Mitigating the Effects of Illumination Variations on the Appearance of Human Targets. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part II. LNCS, vol. 4842, pp. 116–127. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Buchsbaum, G.: A spatial processor model for object color perception. Journal of the Franklin Institute 310(1), 1–26 (1980)CrossRefGoogle Scholar
  14. 14.
    Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  15. 15.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  16. 16.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)CrossRefMATHGoogle Scholar
  17. 17.
    Nadler, B., Lafon, S., Coifman, R.R., Kevrekidis, I.G.: Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators. In: Advances in Neural Information Processing Systems, pp. 955–962 (2005)Google Scholar
  18. 18.
    von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dung Nghi Truong Cong
    • 1
  • Catherine Achard
    • 2
  • Louahdi Khoudour
    • 1
  • Lounis Douadi
    • 1
  1. 1.French National Institute for Transport and Safety Research (INRETS)Villeneuve d’AscqFrance
  2. 2.Institute of Intelligent Systems and Robotics (ISIR)UPMC Univ Paris 06IVRY SUR SEINEFrance

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