Towards a Novel Reidentification Method Using Metaheuristics

  • Tarik LjouadEmail author
  • Aouatif Amine
  • Ayoub Al-Hamadi
  • Mohammed Rziza
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


Tracking multiple moving objects in a video sequence can be formulated as a profile matching problem. Reidentifying a profile within a crowd is done by a matching process between the tracked person and the different moving individuals within the same frame. In that context, the feature matching task can be approximated to a search for the profile that maximizes a considered similarity measure. In this work, we introduce a novel Modified Cuckoo Search (MCS) based reidentification algorithm. A complex descriptor representing each moving person is built from different low level visual features such as the color and the texture components. We make use of a database that involves all previously detected descriptors, forming therefore a discrete search space where the sought solution is a descriptor and its quality is represented by its similarity to the query profile. The approach is evaluated within a multiple object tracking scenario, and a validation process using the normalized cross correlation method to accept or reject the obtained reidentification results is included. The experimental results show promising performances in terms of computation cost as well as reidentification rate.


Object tracking Cuckoo search 


  1. 1.
    S. Bak, E. Corvee, F. Brémond, M. Thonnat, Person re-identification using haar-based and DCD-based signature, in 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (IEEE, New York, 2010), pp. 1–8CrossRefGoogle Scholar
  2. 2.
    B. Benfold, I. Reid, Stable multi-target tracking in real-time surveillance video, in 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2011), pp. 3457–3464Google Scholar
  3. 3.
    E.D. Cheng, M. Piccardi, Matching of objects moving across disjoint cameras, in 2006 IEEE International Conference on Image Processing (IEEE, New York, 2006), pp. 1769–1772CrossRefGoogle Scholar
  4. 4.
    M. Farenzena, L. Bazzani, A. Perina, V. Murino, M. Cristani, Person re-identification by symmetry-driven accumulation of local features, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2010), pp. 2360–2367Google Scholar
  5. 5.
    N. Gheissari, T.B. Sebastian, R. Hartley, Person reidentification using spatiotemporal appearance, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE, New York, 2006), pp. 1528–1535Google Scholar
  6. 6.
    S. Gong, M. Cristani, S. Yan, C.C. Loy, Person Re-identification (Springer, London, 2014). doi: 10.1007/978-1-4471-6296-4 CrossRefGoogle Scholar
  7. 7.
    D. Gray, H. Tao, Viewpoint invariant pedestrian recognition with an ensemble of localized features, in Computer Vision–ECCV 2008 (Springer, Berlin, 2008), pp. 262–275Google Scholar
  8. 8.
    K. Jungling, C. Bodensteiner, M. Arens, Person re-identification in multi-camera networks, in 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (IEEE, New York, 2011), pp. 55–61Google Scholar
  9. 9.
    B. Keni, S. Rainer, Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008, pp. 1–10 (2008)Google Scholar
  10. 10.
    R. Layne, T.M. Hospedales, S. Gong, Re-id: hunting attributes in the wild, in Proceedings of the British Machine Vision Conference (BMVC) (2014)Google Scholar
  11. 11.
    W. Li, R. Zhao, X. Wang, Human reidentification with transferred metric learning, in Computer Vision–ACCV 2012 (Springer, Berlin, 2013), pp. 31–44Google Scholar
  12. 12.
    W. Li, R. Zhao, T. Xiao, X. Wang, Deepreid: deep filter pairing neural network for person re-identification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014), pp. 152–159. doi:10.1109/CVPR.2014.27Google Scholar
  13. 13.
    C.C. Loy, T. Xiang, S. Gong, Multi-camera activity correlation analysis, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009) (2009), pp. 1988–1995. doi:10.1109/CVPR.2009.5206827Google Scholar
  14. 14.
    B. Ma, Y. Su, F. Jurie, Bicov: a novel image representation for person re-identification and face verification, in Proceedings of the British Machine Vision Conference (BMVA Press, Guildford, 2012), pp. 57.1–57.11. Google Scholar
  15. 15.
    O. Oreifej, R. Mehran, M. Shah, Human identity recognition in aerial images, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2010), pp. 709–716Google Scholar
  16. 16.
    W. Ouyang, X. Chu, X. Wang, Multi-source deep learning for human pose estimation, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2014), pp. 2337–2344CrossRefGoogle Scholar
  17. 17.
    B. Prosser, W.S. Zheng, S. Gong, T. Xiang, Q. Mary, Person re-identification by support vector ranking, in Proceedings of the British Machine Vision Conference (BMVC) vol. 3 (2010), p. 5Google Scholar
  18. 18.
    Y. Raja, S. Gong, Scalable multi-camera tracking in a metropolis, in Person Re-identification, Advances in Computer Vision and Pattern Recognition, ed. by S. Gong, M. Cristani, S. Yan, C.C. Loy. (Springer, London, 2014), pp. 413–438. doi: 10.1007/978-1-4471-6296-4-20 Google Scholar
  19. 19.
    D.B. Reid, An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979)CrossRefGoogle Scholar
  20. 20.
    J. Sanchez-Riera, Y.S. Hsiao, T. Lim, K.L. Hua, W.H. Cheng, A robust tracking algorithm for 3d hand gesture with rapid hand motion through deep learning, in 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (IEEE, New York, 2014), pp. 1–6Google Scholar
  21. 21.
    W.R. Schwartz, L.S. Davis, Learning discriminative appearance-based models using partial least squares, in 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI) (IEEE, New York, 2009), pp. 322–329CrossRefGoogle Scholar
  22. 22.
    S. Walton, O. Hassan, K. Morgan, M. Brown, Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9), 710–718 (2011).
  23. 23.
    X. Wang, G. Doretto, T. Sebastian, J. Rittscher, P. Tu, Shape and appearance context modeling, in IEEE 11th International Conference on Computer Vision, ICCV 2007 (IEEE, New York, 2007), pp. 1–8Google Scholar
  24. 24.
    X.S. Yang, S. Deb, Cuckoo search via lévy flights, in Proceedings of World Congress on Nature & Biologically Inspired Computing, NaBIC 2009 (IEEE, New York, 2009), pp. 210–214CrossRefGoogle Scholar
  25. 25.
    X. Yin, Y. Sun, S. Song, X. Ma, A target tracking algorithm based on optical transfer function and normalized cross correlation, in The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems (Springer, Berlin, 2014), pp. 1021–1027Google Scholar
  26. 26.
    R. Zhao, W. Ouyang, X. Wang, Unsupervised salience learning for person re-identification, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tarik Ljouad
    • 1
    Email author
  • Aouatif Amine
    • 2
  • Ayoub Al-Hamadi
    • 3
  • Mohammed Rziza
    • 1
  1. 1.Faculty of Sciences of Rabat (FSR)Mohammed V UniversityRabatMorocco
  2. 2.LGS Laboratory, National School of Applied Sciences (ENSA)Ibn Tofail UniversityKenitraMorocco
  3. 3.Institute for Information Technology and Communications (IIKT)Otto-von-Guericke-University MagdeburgMagdeburgGermany

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