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Accordion Representation Based Multi-scale Covariance Descriptor for Multi-shot Person Re-identification

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10016))

Abstract

Multi-shot person re-identification is a major challenge because of the large variations in a human’s appearance caused by different types of noise such as occlusion, viewpoint and illumination variations. In this paper, we presented the accordion representation based multi-scale covariance descriptor, called AR-MSCOV descriptor, which considers in the first step an image sequence containing a walking human to convert it in one image with the accordion representation. To better exploit the spatial and temporal correlation of the video sequence and to deal with the different types of noise, it applies quadtree decomposition and extracts multi-scale appearance features such as color, gradient and Gabor in a simple pass. This AR-MSCOV descriptor merges the static regions and captures the moving regions of interest. Therefore, it implicitly encodes the described human gait as a behavioral biometric with the appearance features through the accordion representation to reliably identify any person in motion. We evaluated the AR-MSCOV descriptor on the PRID 2011 multi-shot dataset and demonstrated a good performance in comparison with the current state-of-the-art.

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References

  1. Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection. In: Proceedings of 9th European Conference on Computer Vision (2006)

    Google Scholar 

  2. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person reidentification by symmetry-driven accumulation of local features. In: CVPR, pp. 2360–2367 (2010)

    Google Scholar 

  3. Ayedi, W., Snoussi, H., Abid, M.: A fast multi-scale covariance descriptor for object re-identification. Pattern Recogn. Lett. 13(14), 1902–1907 (2012)

    Article  Google Scholar 

  4. 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)

    Article  MATH  Google Scholar 

  5. Bak, S., Bremond, F.: Re-identification by covariance descriptors. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-identification. Advances in Computer Vision and Pattern Recognition, pp. 71–91. Springer, London (2014)

    Chapter  Google Scholar 

  6. Ejaz, A., Michael, J., Tim K.M.: An improved deep learning architecture for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3908–3916 (2015)

    Google Scholar 

  7. Nair, B.M., Kendricks, K.D.: Improved region-based kalman filter for tracking body joints and evaluating gait in surveillance videos. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 311–322. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25903-1_27

    Chapter  Google Scholar 

  8. Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Sig. Process. Mag. 22(6), 78–90 (2005)

    Article  Google Scholar 

  9. Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE TPAMI 34(11), 2164–2176 (2012)

    Article  Google Scholar 

  10. Lombardi, S., Nishino, K., Makihara, Y., Yagi, Y.: Two-point gait: decoupling gait from body shape. In: IEEE International Conference on Computer Vision (ICCV), pp. 1041–1048 (2013)

    Google Scholar 

  11. Ouni, T., Ayedi, W., Abid, M.: New low complexity DCT based video compression method. In: International Conference on Telecommunications, pp. 202–207 (2009)

    Google Scholar 

  12. Abdelhedi, S., Wali, A., Alimi, A.M.: Fuzzy logic based human activity recognition in video surveillance applications. In: Second International Afro-European Conference for Industrial Advancement (AECIA), pp. 227–235 (2015)

    Google Scholar 

  13. Gheissari, N., Sebastian, T., Hartley, R.: Person re-identification using spatiotemporal appearance. In: CVPR, vol. 2, pp. 1528–1535 (2006)

    Google Scholar 

  14. Hadjkacem, B., Ayedi, W., Snoussi, H., Abid, M.: A spatio-temporal covariance descriptor for person re-identification. In: Proceedings of the 15th International Conference on Intelligent Systems Design and Applications, pp. 618–622 (2015)

    Google Scholar 

  15. Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: CVPR, pp. 3318–3325 (2013)

    Google Scholar 

  16. Klaser, A., Marszalek, M.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC (2008)

    Google Scholar 

  17. Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by video ranking. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 688–703. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_45

    Google Scholar 

  18. Bedagkar-Gala, A., Shah, S.K.: Part-based spatio-temporal model for multi-person re-identification. Pattern Recogn. Lett. 33, 1908–1915 (2011)

    Article  Google Scholar 

  19. Bilinski, P., Bremond, F.: Video covariance matrix logarithm for human action recognition in videos. In: International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  20. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl. 29, 328–347 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  21. Pang, Y., Yuan, Y., Li, X.: Gabor-based region covariance matrices for face recognition. IEEE Trans. Circuit Syst. Video Technol. 18(7), 989–993 (2008)

    Article  Google Scholar 

  22. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)

    Article  Google Scholar 

  23. Boudra, S., Yahiaoui, I., Behloul, A.: A comparison of multi-scale local binary pattern variants for bark image retrieval. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 764–775. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25903-1_66

    Chapter  Google Scholar 

  24. Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21227-7_9

    Chapter  Google Scholar 

  25. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition. In: Performance Evaluation of Tracking and Surveillance (2007)

    Google Scholar 

  26. Hirzer, M., Roth, P.M., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 780–793. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_56

    Chapter  Google Scholar 

  27. Karanam, S., Li, Y., Radke, R.J.: Sparse re-id: block sparsity for person re-identification. In: CVPR, pp. 33–40 (2015)

    Google Scholar 

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Acknowledgements

This ‘Mobidoc’ research was achieved through the partnership agreement ‘Programme d’Appui au Système de Recherche et d’Innovation’ (PASRI) between the Government of the Tunisian Republic (ANPR) and the European Union.

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Authors and Affiliations

  1. National Engineers School of Sfax, CES Research Unit, Sfax University, Sfax, Tunisia

    Bassem Hadjkacem, Walid Ayedi & Mohamed Abid

Authors
  1. Bassem Hadjkacem
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  2. Walid Ayedi
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  3. Mohamed Abid
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Corresponding author

Correspondence to Bassem Hadjkacem .

Editor information

Editors and Affiliations

  1. Université Paris-Sud 11 , Orsay, France

    Jacques Blanc-Talon

  2. University of Salento , Lecce, Lecce, Italy

    Cosimo Distante

  3. Ghent University , Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre , Sydney, New South Wales, Australia

    Dan Popescu

  5. University of Antwerp , Wilrijk, Belgium

    Paul Scheunders

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Hadjkacem, B., Ayedi, W., Abid, M. (2016). Accordion Representation Based Multi-scale Covariance Descriptor for Multi-shot Person Re-identification. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-48680-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48679-6

  • Online ISBN: 978-3-319-48680-2

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