Consistent Re-identification in a Camera Network

  • Abir Das
  • Anirban Chakraborty
  • Amit K. Roy-Chowdhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)


Most existing person re-identification methods focus on finding similarities between persons between pairs of cameras (camera pairwise re-identification) without explicitly maintaining consistency of the results across the network. This may lead to infeasible associations when results from different camera pairs are combined. In this paper, we propose a network consistent re-identification (NCR) framework, which is formulated as an optimization problem that not only maintains consistency in re-identification results across the network, but also improves the camera pairwise re-identification performance between all the individual camera pairs. This can be solved as a binary integer programing problem, leading to a globally optimal solution. We also extend the proposed approach to the more general case where all persons may not be present in every camera. Using two benchmark datasets, we validate our approach and compare against state-of-the-art methods.


Person re-identification Network consistency 

Supplementary material

978-3-319-10605-2_22_MOESM1_ESM.pdf (211 kb)
Electronic Supplementary Material (PDF 212 KB)


  1. 1.
    Alavi, A., Yang, Y., Harandi, M., Sanderson, C.: Multi-shot person re-identification via relational stein divergence. In: IEEE International Conference on Image Processing (2013) Google Scholar
  2. 2.
    Avraham, T., Gurvich, I., Lindenbaum, M., Markovitch, S.: Learning implicit transfer for person re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 381–390. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Computer Vision and Image Understanding 117(2), 130–144 (2013)Google Scholar
  4. 4.
    Bellet, A., Habrard, A., Sebban, M.: A survey on metric learning for feature vectors and structured data. ArXiv e-prints (2013)Google Scholar
  5. 5.
    Ben Shitrit, H., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global appearance constraints. In: IEEE International Conference on Computer Vision, pp. 137–144 (2011)Google Scholar
  6. 6.
    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: British Machine Vision Conference (2011)Google Scholar
  9. 9.
    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
  10. 10.
    Gilbert, A., Bowden, R.: Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 125–136. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Javed, O., Shafique, K., Rasheed, Z., Shah, M.: Modeling inter-camera space–time and appearance relationships for tracking across non-overlapping views. Computer Vision and Image Understanding 109(2), 146–162 (2008)CrossRefGoogle Scholar
  12. 12.
    Kviatkovsky, I., Adam, A., Rivlin, E.: Color invariants for person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(7), 1622–1634 (2013)CrossRefGoogle Scholar
  13. 13.
    Li, W., Wang, X.: Locally aligned feature transforms across views. In: IEEE International Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  14. 14.
    Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 31–44. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Liu, C., Gong, S., Loy, C.C., Lin, X.: Person re-identification: What features are important? In: European Conference on Computer Vision, Workshops and Demonstrations, Florence, Italy, pp. 391–401. Springer, Heidelberg (2012)Google Scholar
  16. 16.
    Martinel, N., Micheloni, C.: Re-identify people in wide area camera network. In: International Conference on Computer Vision and Pattern Recognition Workshops, pp. 31–36. IEEE, Providence (2012)Google Scholar
  17. 17.
    Pedagadi, S., Orwell, J., Velastin, S.: Local fisher discriminant analysis for pedestrian re-identification. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3318–3325 (2013)Google Scholar
  18. 18.
    Porikli, F., Hill, M.: Inter-camera color calibration using cross-correlation model function. In: IEEE International Conference on Image Processing (ICIP), pp. 133–136 (2003)Google Scholar
  19. 19.
    Prosser, B., Gong, S., Xiang, T.: Multi-camera matching using bi-directional cumulative brightness transfer functions. In: British Machine Vision Conference (September 2008)Google Scholar
  20. 20.
    Schrijver, A.: Theory of linear and integer programming. John Wiley and Sons (1998)Google Scholar
  21. 21.
    Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 51–65 (2005)CrossRefGoogle Scholar
  22. 22.
    Taj, M., Maggio, E., Cavallaro, A.: Multi-feature graph-based object tracking. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 190–199. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Yang, L., Jin, R.: Distance metric learning: A comprehensive survey. Tech. rep., Michigan State University (2006)Google Scholar
  24. 24.
    Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE International Conference on Computer Vision and Pattern Recognition (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abir Das
    • 1
  • Anirban Chakraborty
    • 1
  • Amit K. Roy-Chowdhury
    • 1
  1. 1.Dept. of Electrical EngineeringUniversity of CaliforniaRiversideUSA

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