Skip to main content

Advertisement

Springer Nature Link
Log in

Cross-dataset person re-identification using deep convolutional neural networks: effects of context and domain adaptation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Over the past years, the impact of surveillance systems on public safety increases dramatically. One significant challenge in this domain is person re-identification, which aims to detect whether a person has already been captured by another camera in the surveillance network or not. Most of the work that has been conducted on person re-identification problem uses a single dataset, in which the training and test data are coming from the same source. However, as we have shown in this work, there is a strong bias among the person re-identification datasets, therefore, a method that has been trained and optimized on a specific person re-identification dataset may not generalize well and perform successfully on the other datasets. This is a problem for many real-world applications, since it is not feasible to collect and annotate sufficient amount of data from the target application to train or fine-tune a deep convolutional neural network model. Taking this issue into account, in this work, we have focused on cross-dataset person re-identification problem and first explored and analyzed in detail the use of the state-of-the-art deep convolutional neural network architectures, namely AlexNet, VGGNet, GoogLeNet, ResNet, and DenseNet that have been developed for generic image classification task. These deep CNN models have been adapted to the person re-identification domain by fine-tuning them for each human body part separately, as well as on the entire body, with the two relatively large person re-identification datasets: CUHK03 and Market-1501. Then, the performance of each adapted model has been evaluated on two different publicly available datasets: VIPeR and PRID2011. We have shown that, even just a domain adaptation leads comparable results to the state-of-the-art cross-dataset approaches. Another point that we have addressed in this paper is context adaptation. It has been known that person re-identification approaches implicitly utilizes background as context information. Therefore, to have a consistent background across different camera views, we have employed the cycle-consistent generative adversarial network. We have shown that this further improves the performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3908–3916

  2. Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T (2017) Looking beyond appearances: synthetic training data for deep CNNs in re-identification. CoRR. arXiv:1701.03153

  3. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: CVPR09

  4. Fan H, Zheng L, Yang Y (2017) Unsupervised person re-identification: clustering and fine-tuning. arXiv:1705.10444

  5. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9):1627–1645

    Article  Google Scholar 

  6. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. Comput. Vis.–ECCV 2008:262–275

    Google Scholar 

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  8. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. CoRR. arXiv:1703.07737

  9. Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Scandinavian conference on image analysis. Springer, pp 91–102

  10. Hu Y, Yi D, Liao S, Lei Z, Li SZ (2014) Cross dataset person re-identification. In: Asian Conference on computer vision. Springer, pp 650–664

  11. Hu J, Lu J, Tan YP (2015) Deep transfer metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 325–333

  12. Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, p 3

  13. Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 2288–2295

  14. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  15. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152–159

  16. Liao S, Hu Y, Li S (2014) Joint dimension reduction and metric learning for person re-identification

  17. Ma AJ, Yuen PC, Li J (2013) Domain transfer support vector ranking for person re-identification without target camera label information. In: Proceedings of the IEEE international conference on computer vision, pp 3567–3574

  18. Ma AJ, Li J, Yuen PC, Li P (2015) Cross-domain person reidentification using domain adaptation ranking svms. IEEE Trans Image Process 24(5):1599–1613

    Article  MathSciNet  Google Scholar 

  19. McLaughlin N, Rincon JMD, Miller P (2015) Data-augmentation for reducing dataset bias in person re-identification. In: 2015 12th IEEE International conference on advanced video and signal based surveillance (AVSS), pp 1–6. https://doi.org/10.1109/AVSS.2015.7301739

  20. Nanda A, Chauhan DS, Sa KP, Bakshi S (2017) Illumination and scale invariant relevant visual features with hypergraph-based learning for multi-shot person re-identification. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-017-4875-7

    Article  Google Scholar 

  21. Nanda A, Sa PK, Choudhury SK, Bakshi S, Majhi B (2017) A neuromorphic person re-identification framework for video surveillance. IEEE Access 5:6471–6482. https://doi.org/10.1109/ACCESS.2017.2686438

    Google Scholar 

  22. Peng P, Xiang T, Wang Y, Pontil M, Gong S, Huang T, Tian Y (2016) Unsupervised cross-dataset transfer learning for person re-identification. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1306–1315. https://doi.org/10.1109/CVPR.2016.146

  23. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  24. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International Conference on learning representations (ICLR)

  25. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  26. Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval

  27. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  28. Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: CVPR 2011, pp 1521–1528. https://doi.org/10.1109/CVPR.2011.5995347

  29. Wu Q (2017) Multi-scale convolutional network for person re-identification. https://doi.org/10.2991/cnct-16.2017.115

  30. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1249–1258

  31. Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: 2014 22nd International conference on pattern recognition (ICPR). IEEE, pp 34–39

  32. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320–3328

  33. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: IEEE International conference on computer vision

  34. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. CoRR. arXiv:1610.02984

  35. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro CoRR

  36. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

  37. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks

Download references

Author information

Authors and Affiliations

  1. Istanbul Technical University, Maslak, Istanbul, Turkey

    Anıl Genç & Hazım Kemal Ekenel

Authors
  1. Anıl Genç
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hazım Kemal Ekenel
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Anıl Genç.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Genç, A., Ekenel, H.K. Cross-dataset person re-identification using deep convolutional neural networks: effects of context and domain adaptation. Multimed Tools Appl 78, 5843–5861 (2019). https://doi.org/10.1007/s11042-018-6409-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6409-3

Keywords