Advertisement

Images Selection and Best Descriptor Combination for Multi-shot Person Re-identification

  • Yousra Hadj HassenEmail author
  • Kais Loukil
  • Tarek Ouni
  • Mohamed Jallouli
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)

Abstract

To re-identify a person is to check if he/she has been already seen over a cameras network. Recently, re-identifying people over large public cameras networks has become a crucial task of great importance to ensure public security. The vision community has deeply studied this area of research. Most existing researches rely only on the spatial appearance information extracted from either one (single-shot) or multiple images (multi-shot) for each person. Actually, the real person re-identification framework is a multi-shot scenario. However, to efficiently model a person’s appearance and to select the most informative samples remain a challenging problem. In this work, an extensive comparison of descriptors of state of art associated to the proposed frame selection method is considered. Specifically, we evaluate the samples selection approach using different known descriptors. For fair comparisons, two standard datasets PRID 2011 and iLIDS-VID are used showing the effectiveness and advantages of the proposed method.

Keywords

Camera network Descriptor Model Multi-shot Person re-identification Selection 

References

  1. 1.
    Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., Radke, R.J.: A comprehensive evaluation and benchmark for person re-identification: features, metrics, and datasets. arXiv preprint arXiv:1605.09653 (2016)
  2. 2.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)
  3. 3.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117, 130–144 (2013)CrossRefzbMATHGoogle Scholar
  4. 4.
    Gheissari, N., Sebastian, T.B., Hartley, R.: Person re-identification using spatiotemporal appearance. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1528–1535. IEEE Press, New York (2006)Google Scholar
  5. 5.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image Analysis, pp. 91–102. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, pp. 262–275. Springer, Marseille (2008)Google Scholar
  7. 7.
    Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2666–2672. IEEE Press, Providence (2012)Google Scholar
  8. 8.
    Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586–3593. IEEE Press, Portland (2013)Google Scholar
  9. 9.
    Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3610–3617. IEEE Press, Portland (2013)Google Scholar
  10. 10.
    Chen, D., Yuan, Z., Chen, B., Zheng, N.: Similarity learning with spatial constraints for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1268–1277. IEEE Press, Las Vegas (2016)Google Scholar
  11. 11.
    Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2528–2535. IEEE Press, Sydney (2013)Google Scholar
  12. 12.
    Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 144–151. IEEE Press, Columbus (2014)Google Scholar
  13. 13.
    Das, A., Chakraborty, A., Roy-Chowdhury, A.K.: Consistent re-identification in a camera network. In: European Conference on Computer Vision, pp. 330–345. Springer, Zurich (2014)Google Scholar
  14. 14.
    Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3318–3325. IEEE Press, Portland (2013)Google Scholar
  15. 15.
    Liu, X., Song, M., Tao, D., Zhou, X., Chen, C., Bu, J.: Semi-supervised coupled dictionary learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3550–3557. IEEE Press, Columbus (2014)Google Scholar
  16. 16.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206. IEEE Press, Boston (2015)Google Scholar
  17. 17.
    Ayedi, W., Snoussi, H., Abid, M.: A fast multi-scale covariance descriptor for object re-identification. Pattern Recogn. Lett. 33, 1902–1907 (2012)CrossRefGoogle Scholar
  18. 18.
    Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124. IEEE Press, Chile (2015)Google Scholar
  19. 19.
    Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical gaussian descriptor for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363–1372. IEEE Press, Las Vegas (2016)Google Scholar
  20. 20.
    Liu, X., Song, M., Zhao, Q., Tao, D., Chen, C., Bu, J.: Attribute restricted latent topic model for person re-identification. Pattern Recogn. 45, 4204–4213 (2012)CrossRefGoogle Scholar
  21. 21.
    Su, C., Yang, F., Zhang, S., Tian, Q., Davis, L.S., Gao, W.: Multi-task learning with low rank attribute embedding for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3739–3747. IEEE Press, Chile (2015)Google Scholar
  22. 22.
    Shi, Z., Hospedales, T.M., Xiang, T.: Transferring a semantic representation for person re-identification and search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4184–4193. IEEE Press, Boston (2015)Google Scholar
  23. 23.
    Lin, S., Ozsu, M.T., Oria, V., Ng, R.: An extensible hash for multi-precision similarity querying of image databases. In: Proceedings of the 27th International Conference on Very Large Databases, Italy, pp. 221–230 (2001)Google Scholar
  24. 24.
    De Avila, S.E.F., Lopes, A.P.B., da Luz, A., de Albuquerque Arajo, A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32, 56–68 (2011)CrossRefGoogle Scholar
  25. 25.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34, 417–435 (2012)CrossRefGoogle Scholar
  27. 27.
    Cong, Y., Yuan, J., Luo, J.: Towards scalable summarization of consumer videos via sparse dictionary selection. IEEE Trans. Multimedia 14, 66–75 (2012)CrossRefGoogle Scholar
  28. 28.
    Elhamifar, E., Sapiro, G., Vidal, R.: Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery. In: Advances in Neural Information Processing Systems, pp. 19–27 (2012)Google Scholar
  29. 29.
    Elhamifar, E., Sapiro, G., Vidal, R.: See all by looking at a few: sparse modeling for finding representative objects. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1600–1607. IEEE Press, Providence (2012)Google Scholar
  30. 30.
    Das, A., Panda, R., Roy-Chowdhury, A.K.: Continuous adaptation of multi-camera person identification models through sparse non-redundant representative selection. Comput. Vis. Image Underst. 156, 66–78 (2016)CrossRefGoogle Scholar
  31. 31.
    Hadj Hassen, Y., Ayedi, W., Ouni, T., Jallouli, M.: Multi-shot person re-identification approach based key frame selection. In: Proceedings of the Eighth International Conference on Machine Vision, International Society for Optics and Photonics, Barcelone, p. 98751H (2015)Google Scholar
  32. 32.
    Corvee, E., Bremond, F., Thonnat, M.: Person re-identification using spatial covariance regions of human body parts. In: Proceedings of the Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 435–440. IEEE Press, Boston (2010)Google Scholar
  33. 33.
    Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by video ranking. In: European Conference on Computer Vision, pp. 688–703. Springer International Publishing, Zurich (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yousra Hadj Hassen
    • 1
    Email author
  • Kais Loukil
    • 1
  • Tarek Ouni
    • 1
  • Mohamed Jallouli
    • 1
  1. 1.National School of Engineers of Sfax, Computer and Embedded Systems LaboratoryUniversity of SfaxSfaxTunisia

Personalised recommendations