Generating Pedestrian Images for Person Re-identification

  • Zhong ZhangEmail author
  • Tongzhen Si
  • Shuang Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Person re-identification (re-ID) is mainly used to search the target pedestrian in different cameras. In this paper, we employ generative adversarial network (GAN) to expand training samples and evaluate the performance of two different label assignment strategies for the generated samples. We also investigate how the number of generated samples influences the re-ID performance. We do several experiments on the Market1501 database, and the experimental results are of essential reference value to this research field.


Person re-identification GAN Generated samples 



This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, 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.


  1. 1.
    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; 2013. p. 2690–7.Google Scholar
  2. 2.
    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
  3. 3.
    Liao S, Hu Y, Zhu X, Li ZS. Person re-identification by local maximal occurrence representation and metric learning. In: IEEE conference on computer vision and pattern recognition. Boston; 2015. p. 2197–206.Google Scholar
  4. 4.
    Koestinger M, Hirzer M, Wohlhart P, Peter M, Horst B. Large scale metric learning from equivalence constraints. In: IEEE conference on computer vision and pattern recognition. Providence; 2012. p. 2288–95.Google Scholar
  5. 5.
    Bazzani L, Cristani M, Murino V. Symmetry-driven accumulation of local features for human characterization and re-identification. Comput Vis Image Underst. 2013;117(2):130–44.CrossRefGoogle Scholar
  6. 6.
    Ma B, Su Y, Jurie F. Local descriptors encoded by fisher vectors for person re-identification. In: European conference on computer vision. Firenze ; 2012. p. 413–22.CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Zhang Z, Si T. Learning deep features from body and parts for person re-identification in camera networks. EURASIP J Wirel Commun Network. 2018;52.Google Scholar
  9. 9.
    Zheng Z, Zheng L, Yang Y. A discriminatively learned cnn embedding for person re-identification. ACM Trans Multimedia Comput Commun Appl. 2017;14(1):13.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Sun Y, Zheng L, Yang Y, Tian Q, Wang S. Beyond part models: person retrieval with refined part pooling; 2017. arXiv preprint arXiv:1711.09349.
  11. 11.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems. Montreal; 2014. p. 2672–80.Google Scholar
  12. 12.
    Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by Gan improve the person re-identification baseline in vitro; 2017. arXiv preprint arXiv:1701.07717.
  13. 13.
    Zhong Z, Zheng L, Zheng Z, Li S, Yang Y. Camera style adaptation for person re-identification. In: IEEE conference on computer vision and pattern recognition; 2018.Google Scholar
  14. 14.
    Deng W, Zheng L, Kang G, Yang Y, Ye Q, Jiao J. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification; 2017. arXiv:1711.07027.
  15. 15.
    Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks; 2015. arXiv preprint arXiv:1511.06434.
  16. 16.
    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. Chile; 2015. p. 1116–24.Google Scholar
  17. 17.
    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; 2016. p. 770–8.Google Scholar
  18. 18.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M. Imagenet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211–52.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Felzenszwalb P, Girshick R, McAllester D, 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

  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina
  2. 2.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

Personalised recommendations