Skip to main content
SpringerLink
Log in

Generating Pedestrian Images for Person Re-identification

  • Conference paper
  • First Online:
Book cover Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

  • 2170 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  MathSciNet  Google Scholar 

  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. 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. 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. 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. 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. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks; 2015. arXiv preprint arXiv:1511.06434.

  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. 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. 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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Zhong Zhang, Tongzhen Si & Shuang Liu

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Zhong Zhang, Tongzhen Si & Shuang Liu

Authors
  1. Zhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tongzhen Si
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Z., Si, T., Liu, S. (2020). Generating Pedestrian Images for Person Re-identification. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation