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Evaluation Distance Metrics for Pedestrian Retrieval

  • Zhong ZhangEmail author
  • Meiyan Huang
  • Shuang Liu
  • Tariq S. Durrani
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

Pedestrian retrieval is an important technique of searching for a specific pedestrian from a large gallery. In this paper, we introduce three types of distance metrics for pedestrian retrieval, including learning-free distance metric methods, metric learning methods, and convolution neural network (CNN) methods, and evaluate the performance of different distance metrics using the Market-1501 database. The experiment shows that the CNN methods achieve the best results.

Keywords

Pedestrian retrieval Learning-free distance metric methods Metric learning methods CNN methods 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61711530240 and No. 61501327, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600 and No. 15JCQNJC01700, the Fund of Tianjin Normal University under Grant No.135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001 and No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, the NSFC-Royal Society grant, and the Tianjin Higher Education Creative Team Funds Program.

References

  1. 1.
    Zheng F, Shao L. Learning cross-view binary identities for fast person re-identification. In: International joint conference on artificial intelligence. New York: USA; 2016. p. 2399–406.Google Scholar
  2. 2.
    Chen J, Wang Y, Qin J, Liu L, Shao L. Fast person re-identification via cross-camera semantic binary transformation. In: IEEE conference on computer vision and pattern recognition. Honolulu, HI, USA; 2017. p. 5330–9.Google Scholar
  3. 3.
    Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C. Cross-view action recognition via A continuous virtual path. In: IEEE conference on computer vision and pattern recognition. Portland, OR, USA; 2013. p. 2690–7.Google Scholar
  4. 4.
    Zhang Z, Wang C, Xiao B, Zhou W, Liu S. Action recognition using context-constrained linear coding. IEEE Signal Proc Let. 2012;19(7):439–42.CrossRefGoogle Scholar
  5. 5.
    Zhang Z, Wang C, Xiao B, Zhou W, Liu S. Attribute regularization based human action recognition. IEEE T Inf Foren Sec. 2013;8(10):1600–9.CrossRefGoogle Scholar
  6. 6.
    Farenzena M, Bazzani L, Perina A, Murino V, Cristani M. Person re-identification by symmetry-driven accumulation of local features. In: IEEE conference on computer vision and pattern recognition. San Francisco, CA, USA; 2010. p. 2360–7.Google Scholar
  7. 7.
    Zhang D, Lu G. Evaluation of similarity measurement for image retrieval. In: International conference on neural networks and signal processing. Nanjing, China; 2003. p. 928–31.Google Scholar
  8. 8.
    Cheng D, Cristani M, Stoppa M, Bazzani L, Murino V. Custom pictorial structures for re-identification. In: British machine vision conference. Dundee, UK; 2011. p. 6.Google Scholar
  9. 9.
    Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q. Scalable person re-identification: a benchmark. In: IEEE international conference on computer vision. Santiago, Chile; 2015. p. 1116–24.Google Scholar
  10. 10.
    Xing E, Jordan M, Russell S, Ng A. Distance metric learning with application to clustering with side-information. In: Advances in neural information processing systems. Vancouver, British Columbia, Canada; 2003. p. 521–8.Google Scholar
  11. 11.
    Weinberger K, Blitzer J, Saul K. Distance metric Learning for large margin nearest neighbor classification. In: Advances in neural information processing systems. Vancouver, British Columbia, Canada; 2006. p. 1473–80.Google Scholar
  12. 12.
    Davis J, Kulis B, Jain P, Sra S, Dhillon I. Information-theoretic metric learning. In: International conference on machine learning. Cincinnati, Ohio, USA; 2007. p. 209–16.Google Scholar
  13. 13.
    Guillaumin M, Verbeek J, Schmid C. Is that you? Metric learning approaches for face identification. In: IEEE international conference on computer vision. Berthold K.P. Horn; 2009. p. 498–505.Google Scholar
  14. 14.
    Koestinger M, Hirzer M, Wohlhart P, Roth P, Bischof H. Large scale metric learning from equivalence constraints. In: IEEE conference on computer vision and pattern recognition. Providence, RI, USA; 2012. p. 2288–95.Google Scholar
  15. 15.
    Liao S, Hu Y, Zhu X, Li S. Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on computer vision and pattern recognition. Boston, Massachusetts; 2015. p. 2197–206.Google Scholar
  16. 16.
    Zheng Z, Zheng L, Yang Y. A Discriminatively learned CNN embedding for person re-identification. ACM T Multim Comput. 2017;14(1):13.MathSciNetGoogle Scholar
  17. 17.
    Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identificationn. 2017. arXiv:1703.07737.
  18. 18.
    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition. Las Vegas, Nevada; 2016. p. 770–8.Google Scholar
  19. 19.
    Zhang Z, Huang M. Learning local embedding deep features for person re-identification in camera networks. Eurasip J Wirel Comm. 2018;1–9.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Zhong Zhang
    • 1
    • 2
    Email author
  • Meiyan Huang
    • 1
    • 2
  • Shuang Liu
    • 1
    • 2
  • Tariq S. Durrani
    • 3
  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina
  2. 2.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina
  3. 3.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgow ScotlandUK

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