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Person Re-Identification pp 309–330Cite as

Re-identification for Improved People Tracking

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

Abstract

Re-identification is usually defined as the problem of deciding whether a person currently in the field of view of a camera has been seen earlier either by that camera or another. However, a different version of the problem arises even when people are seen by multiple cameras with overlapping fields of view. Current tracking algorithms can easily get confused when people come close to each other and merge trajectory fragments into trajectories that include erroneous identity switches. Preventing this means re-identifying people across trajectory fragments. In this chapter, we show that this can be done very effectively by formulating the problem as a minimum-cost maximum-flow linear program. This version of the re-identification problem can be solved in real-time and produces trajectories without identity switches. We demonstrate the power of our approach both in single- and multicamera setups to track pedestrians, soccer players, and basketball players.

This work was funded in part by the Swiss National Science Foundation.

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References

  1. Abramson, Y., Steux, B., Ghorayeb, H.: YEF Real-time object detection. In: International Workshop on Automatic Learning and Real-Time (ALaRT) (2005)

    Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikïinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  3. Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  4. Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  5. Andriyenko, A., Schindler, K.: Globally optimal multi-target tracking on a hexagonal lattice. In: European Conference on Computer Vision (2010)

    Google Scholar 

  6. Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  7. APIDIS European Project FP7-ICT-216023: (2008–2010). www.apidis.org

  8. Barinova, O., Lempitsky, V., Kohli, P.: On detection of multiple object instances using hough transforms. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1773–1784 (2012)

    Google Scholar 

  9. Bazaraa, M.S., Jarvis, J.J., Sherali, H.D.: Linear programming and network flows. Wiley, Hiedelberg (2010)

    Google Scholar 

  10. BenShitrit, H., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global apperance constraints. In: International Conference on Computer Vision (2011)

    Google Scholar 

  11. BenShitrit, H., Berclaz, J., Fleuret, F., Fua, P.: Multi-commodity network flow for tracking multiple people. IEEE Transactions on Pattern Analysis and Machine Intelligence (2013). Submitted for publication. Available as technical report EPFL-ARTICLE-181551.

    Google Scholar 

  12. Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using K-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011). http://cvlab.epfl.ch/software/ksp

    Google Scholar 

  13. Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. (2008)

    Google Scholar 

  14. Bimbo, A.D., Lisanti, G., Masi, I., Pernici, F.: Person detection using temporal and geometric context with a Pan Tilt zoom camera. In: International Conference on Pattern Recognition (2010)

    Google Scholar 

  15. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

  16. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  17. Dantzig, G.B.: Linear programming and extensions. Princeton University Press, Princeton (1963)

    Google Scholar 

  18. D’Orazio, T., Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P.L.: A semi-automatic system for ground truth generation of soccer video sequences. In: International Conference on Advanced Video and Signal Based Surveillance (2009)

    Google Scholar 

  19. Ellis, A., Shahrokni, A., Ferryman, J.: Pets 2009 and winter pets 2009 results, a combined evaluation. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2009)

    Google Scholar 

  20. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Knowledge Discovery and Data Mining (1996)

    Google Scholar 

  21. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Google Scholar 

  22. Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008). http://cvlab.epfl.ch/software/pom

    Google Scholar 

  23. Jiang, H., Fels, S., Little, J.: A linear programming approach for multiple object tracking. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  24. Karmarkar, N.: A new polynomial time algorithm for linear programming. Combinatorica 4(4), 373–395 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  25. Misu, T., Matsui, A., Clippingdale, S., Fujii, M., Yagi, N.: Probabilistic integration of tracking and recognition of soccer players. In: Advances in Multimedia Modeling (2009)

    Google Scholar 

  26. Perera, A., Srinivas, C., Hoogs, A., Brooksby, G., Wensheng, H.: Multi-object tracking through simultaneous long occlusions and split-merge conditions. In: Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  27. Pirsiavash, H., Ramanan, D.: Steerable part models. In: Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  28. Pirsiavash, H., Ramanan, D., Fowlkes, C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: Conference on Computer Vision and Pattern Recognition (2011). http://www.ics.uci.edu/%7edramanan/

  29. Storms, P., Spieksma, F.: An LP-based algorithm for the data association problem in multitarget tracking. Computers and Operations Research 30(7), 1067–1085 (2003)

    Google Scholar 

  30. Suurballe, J.W.: Disjoint paths in a network. Networks 4, 125–145 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  31. Tsai, R.: A versatile cameras calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. Int. J. Robot. Autom. 3(4), 323–344 (1987)

    Article  Google Scholar 

  32. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  33. Wu, T., Lin, C.: Weng, R. C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)

    Google Scholar 

  34. Yuan, L., Bo, W., Nevatia, R.: Human detection by searching in 3D space using camera and scene knowledge. In: International Conference on Pattern Recognition (2008)

    Google Scholar 

  35. Zervos, M., BenShitrit, H., Fleuret, F., Fua, P.: Facial descriptors for identity-preserving multiple people tracking. Tech. Rep. EPFL-REPORT-187534, EPFL (2013)

    Google Scholar 

  36. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. IDIAP, Martigny, CH-1920, Switzerland

    François Fleuret

  2. EPFL, Lausanne, CH-1015, Switzerland

    Horesh Ben Shitrit & Pascal Fua

Authors
  1. François Fleuret
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  2. Horesh Ben Shitrit
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  3. Pascal Fua
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Corresponding author

Correspondence to François Fleuret .

Editor information

Editors and Affiliations

  1. Queen Mary University, London, United Kingdom

    Shaogang Gong

  2. University of Verona, Italy

    Marco Cristani

  3. National University of Singapore, Singapore

    Shuicheng Yan

  4. The Chinese University of Hong Kong, Shatin, Hong Kong SAR

    Chen Change Loy

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© 2014 Springer-Verlag London

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Fleuret, F., Shitrit, H.B., Fua, P. (2014). Re-identification for Improved People Tracking. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_15

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_15

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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