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Evaluation of Local Features Using Convolutional Neural Networks for Person Re-Identification

  • Shuang LiuEmail author
  • Xiaolong Hao
  • Zhong Zhang
  • Mingzhu Shi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

In this paper, we mainly evaluate the influence of local features extracted by convolutional neural networks for person re-identification. Considering the variant body parts with different structural information, we divide the holistic person images into several parts and extract their features. Two kinds of aggregation methods are used to aggregate local features. Experiments on the challenging person re-identification database, Market-1501 database, show that the max aggregation is more effective for extracting the discriminative local features than the sum aggregation.

Keywords

Local features Convolutional neural networks Person re-identification 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61501327, No. 61711530240 and No. 61501328, 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, and the Tianjin Higher Education Creative Team Funds Program.

References

  1. 1.
    Liao S, Hu Y, Zhu X, Li SZ. Person re-identification by local maximal occurrence representation and metric learning. In: IEEE conference on computer vision and pattern recognition; 2015. p. 2197–206.Google Scholar
  2. 2.
    Sathish PK, Balaji S. Person re-identification in surveillance videos using multi-part color descriptor. Int J Comput Appl. 2015;121(16):15–7.Google Scholar
  3. 3.
    Zhang R, Liang L, Zhang R, Wang M, Zhang L. Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process. 2015;24(12):4766–79.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang Z, Wang C, Xiao B, Zhou W, Liu S. Action recognition using context-constrained linear coding. IEEE Sig Process Lett. 2012;19(7):439–42.CrossRefGoogle Scholar
  5. 5.
    Zheng L, Zhang H, Sun S, Chandraker M, Yang Y, Tian Q. Person re-identification in the wild. In: IEEE conference on computer vision and pattern recognition; 2017. p. 1367–76.Google Scholar
  6. 6.
    Zheng L, Yang Y, Hauptmann AG. Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984; 2016.
  7. 7.
    Wu L, Shen C, Hengel AVD. Personnet: person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255; 2016.
  8. 8.
    Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X. Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: IEEE conference on computer vision and pattern recognition; 2017. p. 1077–85.Google Scholar
  9. 9.
    Zhang Z, Wang C, Xiao B, Zhou W, Liu S. Attribute regularization based human action recognition. IEEE Trans Inf Forensics Secur. 2013;8(10):1600–9.CrossRefGoogle Scholar
  10. 10.
    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; 2013. p. 2690–7.Google Scholar
  11. 11.
    Ma B, Su Y, Jurie F. Local descriptors encoded by fisher vectors for person re-identification. In: European conference on computer vision; 2012. p. 413–22.CrossRefGoogle Scholar
  12. 12.
    Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: International conference on computer vision; 2017. p. 3774–82.Google Scholar
  13. 13.
    Xiao T, Li H, Ouyang W, Wang X. Learning deep feature representations with domain guided dropout for person re-identification. In: IEEE conference on computer vision and pattern recognition; 2016. p. 1249–58.Google Scholar
  14. 14.
    Zheng Z, Zheng L, Yang Y. A discriminatively learned cnn embedding for person reidentification. ACM Trans Multimedia Comput Commun Appl. 2017;14(1):1–20.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Yi D, Lei Z, Liao S, Li SZ. Deep metric learning for practical person re-identification. In: International conference on pattern recognition; 2014. p. 34–9.Google Scholar
  16. 16.
    Sun Y, Zheng L, Yang Y, Tian Q, Wang S. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). arXiv preprint arXiv:1711.09349; 2018.
  17. 17.
    Dikmen M, Akbas E, Huang TS, Ahuja N. Pedestrian recognition with a learned metric. In: Asian conference on computer vision; 2010. p. 501–12.CrossRefGoogle Scholar
  18. 18.
    Xiang S, Nie F, Zhang C. Learning a mahalanobis distance metric for data clustering and classification. Pattern Recognit. 2008;41(12):3600–12.CrossRefGoogle Scholar
  19. 19.
    Cheng D, Gong Y, Zhou S, Wang J, Zheng N. Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: IEEE conference on computer vision and pattern recognition; 2016. p. 1335–44.Google Scholar
  20. 20.
    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; 2015. p. 1116–24.Google Scholar
  21. 21.
    Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Neural information processing systems; 2012. p. 1097–105.Google Scholar
  22. 22.
    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations; 2015.Google Scholar
  23. 23.
    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition; 2016. p. 770–8Google Scholar
  24. 24.
    Felzenszwalb PF, Girshick RB, Mcallester DA, Ramanan D. Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell. 2010;32(9):1627–45.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shuang Liu
    • 1
    • 2
    Email author
  • Xiaolong Hao
    • 1
    • 2
  • Zhong Zhang
    • 1
    • 2
  • Mingzhu Shi
    • 1
    • 2
  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina
  2. 2.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

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