Skip to main content
SpringerLink
Log in

Novel Similarity Metric Learning Using Deep Learning and Root SIFT for Person Re-identification

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper deals with the person re-identification, intending to match the images of the person captured using disjoint cameras mounted in different locations. Such a task of matching the images remains a difficult issue as the appearance of the individual differs from the perspective of the various cameras. Inspired by the recent success of deep learning in the domain of person re-identification, a novel deep learning framework which combines deep features and Root Scale Invariant Features Transform (Root SIFT) features has been proposed. The conventional deep Convolutional Neural Network (CNN) can obtain significant features but does not take into account the spatial relationship between the features. Also, CNN requires an enormous number of instances to train the network. To address these issues, the proposed method combines Root SIFT features along with the CNN features. With the combination of Deep and Root SIFT features, the model can give improved performance over other CNN based models. Experiments were conducted on standard datasets CUHK 03 (labelled and detected), CUHK 01 and VIPeR and the matching rate is reported as 74.45% for CUHK 03 (labelled), 72.63% for CUHK 03 (detected), 76.12% for CUHK 01 and 48.45% for VIPeR dataset. The experiments demonstrate that the proposed algorithm has improved identification rate over the recent algorithms.

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

Access this article

Log in via an institution

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
Fig. 9

Similar content being viewed by others

References

  1. Porikli, F. (2003). Inter-camera color calibration by correlation model function. In Proceedings of the international conference on image processing. ICIP ‘03 (pp. II−133). Barcelona, Spain: IEEE.

  2. Javed, O., Shafique, K., & Shah, M. (2005). Appearance modeling for tracking in multiple non-overlapping cameras. In Proceedings of the computer society conference on computer vision and pattern recognition. CVPR ‘05 (pp. 26–33). San Diego, CA: IEEE.

  3. Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011). Person re-identification by descriptive and discriminative classification. In Proceedings of the Scandinavian conference on image analysis (pp. 91–102). Berlin, Heidelberg: Springer.

  4. Gijsenij, A., Lu, R., & Gevers, T. (2012). Colour constancy for multiple light sources. IEEE Transactions on Image Processing, 21(2), 697–707.

    Article  MathSciNet  Google Scholar 

  5. Kviatkovsky, Adam, A., & Rivlin, E. (2012). Colour invariants for person reidentification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1622–1634.

    Article  Google Scholar 

  6. Kuo, C. H., Khamis, S., & Shet, V. (2013). Person re-identification using semantic colour names and rank boost. In Proceedings of the workshop on applications of computer vision. WACV ‘13 (pp. 281–287). Clearwater Beach, FL: IEEE.

  7. Li, P., Wu, H., Chen, Q., & Bi, C. (2015). Person re-identification using colour enhancing feature. In Proceedings of the 3rd IAPR Asian conference on pattern recognition (ACPR) (pp. 086–090). Kuala Lumpur, Malaysia: IEEE. https://doi.org/10.1109/ACPR.2015.7486471.

  8. Varior, R. R., Wang, G., Lu, J., & Liu, T. (2016). Learning invariant colour features for person reidentification. IEEE Transactions on Image Processing, 25(7), 3395–3410.

    Article  MathSciNet  Google Scholar 

  9. Bak, S., Corvee, E., Bremond, F., & Thonnat, M. (2010), Person reidentification using haar-based and DCD-based signature. In Proceedings of the advanced video and signal based surveillance (pp. 1–8). Boston, MA, USA.

  10. Chahla, C., Snoussi, H., Abdallah, F., & Dornaika, F. (2017). Discriminant quaternion local binary pattern embedding for person re-identification through prototype formation and colour categorization. Engineering Applications of Artificial Intelligence, 58, 27–33.

    Article  Google Scholar 

  11. Zhao, R., Ouyang, W., & Wang, X. (2013). Unsupervised salience learning for person re-Identification. In Proceedings of the conference on computer vision and pattern recognition. CVPR ‘13 (pp. 3586–3593). Portland, Oregon: IEEE.

  12. Zhao, R., Ouyang, W., & Wang, X. (2013). Person re-identification by salience matching. In Proceedings of the international conference on computer vision. ICCV ‘13 (pp. 2528–2535). Sydney, Australia: IEEE.

  13. Farenzena, M., Bazzani, L., Perina, A., Murino, V., & Cristani, M. (2010). Person re-identification by symmetry-driven accumulation of local features. In Proceedings of the computer society conference on computer vision and pattern recognition (pp. 2360–2367). San Francisco, California: IEEE.

  14. Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012). Large scale metric learning from equivalence constraints. In Proceedings of the Computer Vision and Pattern Recognition. CVPR ‘12. (pp. 2288–2295). IEEE: Providence, RI, USA.

  15. Hirzer, M., Beleznai, C., Kostinger, M., Roth, P. M., & Bischof, H. (2012). Dense appearance modeling and efficient learning of camera transitions for person re-identification. In Proceedings of the 19th international conference on image processing (pp. 1617–1620). Los Vegas, Nevada: IEEE.

  16. Zheng, W. S., Gong, S., & Xiang, T. (2012). Reidentification by relative distance comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), 653–668.

    Article  Google Scholar 

  17. Leng, Q. (2018). Co-metric learning for person re-identification. Advances in Multimedia. https://doi.org/10.1155/2018/3586191.

  18. Yang, X., Wang, M., & Tao, D. (2017). Person re-identification with metric learning using privileged information. IEEE Transactions on Image Processing, 27(2), 791–805.

    Article  MathSciNet  Google Scholar 

  19. Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014). Deep metric Learning for Person Re-identification. In Proceedings of the 22nd international conference on pattern recognition (pp. 34–39). Stockholm, Sweden: IEEE.

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

  21. Chaudhary, D. D., & Jadhav, N. (2018). Learning invariant colour features for person reidentification. International Journal of Engineering Technologies and Management Research, 5(5), 65–70.

    Article  Google Scholar 

  22. Qian, X., Fu, Y., Jiang, Y. G., Xiang, T., & Xue, X. (2017). Multi-scale deep learning architectures for person re-identification. In Proceedings of the international conference on computer vision (pp. 5399–5408). Venice, Italy: IEEE.

  23. Zheng, M., Karanam, S., Wu, Z., & Radke, R. J. (2019). Re-identification with consistent attentive siamese networks. In Proceedings of the conference on computer vision and pattern recognition (pp. 5735–5744). Long Beach, CA: IEEE.

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

  25. Li, D. X., Fei, G. Y., & Teng, S. W. (2020). Learning large margin multiple granularity features with an improved siamese network for person re-identification. Symmetry, 12(1), 92–99.

    Article  Google Scholar 

  26. Yan, Y., Ni, B., Song, Z., Ma, C., Yan, Y., & Yang, X. (2016). Person re-identification via recurrent feature aggregation. In European conference on computer vision (pp. 701–716). Cham: Springer.

  27. Sang, H., Wang, C., He, D., & Qing, L. (2019) Multi-information flow CNN and attribute-aided reranking for person reidentification. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2019/7028107.

  28. Yang, Y. X., Wen, C., Xie, K., Wen, F. Q., Sheng, G. Q., & Tang, X. G. (2018). Face recognition using the SR-CNN model. Sensors (Basel, Switzerland), 18(12), 4237–4243.

    Article  Google Scholar 

  29. Sang, H., Wang, C., He, D., & Liu, Q. (2019). View confusion feature learning for person re-identification. In Proceedings of the international conference on computer vision (pp. 6639–6648). Seoul, Korea: IEEE.

  30. Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the international conference on machine learning (Vol. 30(1), pp. 3–12).

  31. Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In Proceedings of the computer society conference on computer vision and pattern recognition. CVPR ‘06. 2 (pp. 1735–1742). New York, NY: IEEE.

  32. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Proceedings of the 7th international conference on computer vision. (Vol. 2, pp. 1150–1157). IEEE.

  33. Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. In European conference on computer vision (pp. 430–443). Berlin, Heidelberg: Springer.

  34. Arandjelović, R., & Zisserman, A. (2012). Three things everyone should know to improve object retrieval. In Proceedings of the conference on computer vision and pattern recognition (pp. 2911–2918). Massachusetts Ave., NW: IEEE.

  35. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In Proceedings of the international conference on computer vision (pp. 1116–1124). Santiago, Chile: IEEE.

  36. Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015). Person Re-Identification by Local Maximal Occurrence Representation and Metric Learning. In Proceedings of the Conference on Computer Vision and Pattern Recognition (pp. 2197–2206). IEEE: Boston, MA.

  37. Zhang, J., Hu, X., Wang, M., Qiao, H., Li, X., & Sun, T. (2019). Person re-identification via group symmetry theory. IEEE access : practical innovations, open solutions, 7, 133686–133693.

    Article  Google Scholar 

  38. Qi, L., Wang, L., Huo, J., Shi, Y., & Gao, Y. (2019). GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification. arXiv preprint arXiv:1908.05142http://arxiv.org/1908.05142.

  39. Li, W., Zhao, R., & Wang, X. (2012). Human Reidentification With Transferred Metric Learning. In Asian Conference on Computer Vision. (pp. 31–44). Springer: Berlin, Heidelberg.

Download references

Author information

Authors and Affiliations

  1. Department of CSE, SRMIST, Kattangulathur, Chennai, India

    M. K. Vidhyalakshmi & E. Poovammal

  2. Department of Electrical and Computer Engineering, San Francisco State University, San Francisco, CA, USA

    Vidhyacharan Bhaskar

  3. R & D, Dev Technologies, Chennai, India

    J. Sathyanarayanan

Authors
  1. M. K. Vidhyalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Poovammal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vidhyacharan Bhaskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Sathyanarayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vidhyacharan Bhaskar.

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

Vidhyalakshmi, M.K., Poovammal, E., Bhaskar, V. et al. Novel Similarity Metric Learning Using Deep Learning and Root SIFT for Person Re-identification. Wireless Pers Commun 117, 1835–1851 (2021). https://doi.org/10.1007/s11277-020-07948-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07948-1

Keywords

Search

Navigation