Skip to main content

Advertisement

SpringerLink
Log in

Person re-identification using prioritized chromatic texture (PCT) with deep learning

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

Abstract

Person re-identification (re-ID) helps to identify a person’s attention in different cameras. But this is not an easy task, due to distance, illumination and lack of dataset. Nowadays, this field attracts many researchers because of its varied applications. Here, the information of both local texture and global color representations are concatenated with an original raw image. This concatenated information is gathered by finding the maximum value of chrominance in terms of HSV, texture in terms of Scale Invariant Local Ternary Pattern (SILTP) for each pixel and original raw image. SILTP is well known for its illumination invariant texture description. Convolutional Neural Network (CNN) is used in the proposed work to extract the features from the concatenated information. The proposed Prioritized Chromatic Texture Image (PCTimg) is concatenated with original raw image and fed into CNN. Here, finally a six dimensionalfeature is fed into CNN to extract the deep features. Cross-view Quadratic Discriminant Analysis (XQDA) similarity metric algorithm is employed to re-identify a person.Multiscale Retinex algorithm is used for pre-processing the images.To address the challenges in terms of view point deflection, a sliding window is formed for describing local details of a person in the SILTP feature extraction phase. The HSV helps to incorporate the human color perception. The triplet loss function is used to learn the similarity and the dissimilarity of the training images. The performance analysis of the proposed work is improvedwhen compared to the existingworks.

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

Similar content being viewed by others

References

  1. E. Ahmed, Michael Jones, and Tim K Marks (2015). “An improved deep learning architecture for person reidentification”, In CVPR

  2. Ani Brown Mary N, Dharma D (2019) A novel framework for real-time diseased coral reef image classification. Springer, Multimedia Tools Appl 78:11387–11425

    Article  Google Scholar 

  3. D Chen, Z Yuan, G Hua, N Zheng, and J Wang (2015). “Similarity learning on an explicit polynomial kernel feature map for person re-identification,” in Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1565–1573

  4. S Ding, L Lin, G Wang, and H Chao (2015). “Deep feature learning with relative distance comparison for person re-identification”, pattern recognition, Vol. 48

  5. Shaogang Gong, Marco Cristani, Shuicheng Yan, and Chen Change Loy (2014). “Person re-identification”, Springer, January

  6. D Gray, S Brennan, and H Tao (2007). “Evaluating appearance models for recognition, reacquisition, and tracking”, In IEEE International workshop on performance evaluation of tracking and surveillance

  7. Douglas Gray and Hai Tao (2008). “Viewpoint invariant pedestrian recognition with an ensemble of localized features”. In ECCV

  8. D Gray and H Tao (2008). “Viewpoint invariant pedestrian recognition with an ensemble of localized features,” in European conference on computer vision. Springer, pp. 262–275

  9. HamedAlqahtani, ManolyaKavakli-Thorne, Charles Z. Liu (2019). “An Introduction to Person Re-identification with Generative Adversarial Networks”, arXiv:1904.05992

  10. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing coadaptation of feature detectors. Comput Therm Sci 3(4):212–223

    Google Scholar 

  11. Jingyi LV, Zhiyong Li, Ke Nai, Ying Chen and Jin Yuan (2020). “Person re-identification with expanded neighbourhoods distance re-ranking”, Elsevier, Image and Vision Computing, Vol. 95

  12. Jobson DJ, Rahman ZU, Woodell GA (1997) Properties and performance of a center/surround Retinex. Image Processing, IEEE Transactions on 6(3):451–462

    Article  Google Scholar 

  13. S Karanam, Y Li, and RJ Radke (2015). “Person re-identification with discriminatively trained viewpoint invariant dictionaries”, In ICCV, pp. 4516–4524

  14. S Khamis, C Kuo, V Singh, V Shet, and L Davis (2014). “Joint learning for attribute-consistent person re-identification”, In ECCV Workshop on Visual Surveillance and Reidentification

  15. Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof (2012). “Large scale metric learning from equivalence constraints”, In CVPR

  16. A Krizhevsky, I Sutskever, and GE Hinton (2012). “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, pp. 1097–1105

  17. Kviatkovsky I, Adam A, Rivlin E (2013) Color invariants for person reidentification. TPAMI 35(7):1622–1634

    Article  Google Scholar 

  18. R Layne, TM Hospedales, and S Gong (2013). “Domain transfer for person re-identification”, In Proceedings of the 4th ACM/IEEE international workshop on analysis and retrieval of tracked events and motion in imagery stream

  19. Z Li, S Chang, F Liang, TS Huang, L Cao, and JR Smith (2013). “Learning locally-adaptive decision functions for person verification”, In CVPR, pp. 3610–3617

  20. Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang (2014). Deepreid,“Deep filter pairing neural network for person re-identification”, In CVPR

  21. Liang Zheng, Yi Yang, and Alexander G. Hauptmann (2015). “Person Re-identification: Past, Present and Future”, Journal Of Latex Class Files, Vol. 14, No. 8, August

  22. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In CVPR:2197–2206

  23. S Liao and SZ Li (2015). “Efficient psd constrained asymmetric metric learning for person re-identification”, In ICCV, pp. 3685–3693

  24. CC Loy, T Xiang, and S Gong (2009). “Multi-camera activity correlation analysis,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1988–1995

  25. Lu X, Ni B, Ma C, Yang X (2019) Learning transform-aware attentive network for object tracking. Elsevier, Neuro computing 349:133–144

    Google Scholar 

  26. Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao and Fatih Porik n.d.. “See more, know more: unsupervised video object segmentation with co-attention Siamese networks” IEEE, CVPR

  27. AJ Ma, J Li, PC Yuen, and P Li (2015). “Cross-domain person reidentification using domain adaptation ranking svms”, IEEETransactions on image processing, Vol.24

  28. B Ma, Y Su, and F Jurie Bicov (2012). “A novel image representation for person re-identification and face verification”, In BMVC

  29. L Ma, X Yang, and D Tao (2014). “Person re-identification over camera networks using multi-task distance metric learning”, IEEE transactions on image processing, Vol. 23

  30. Ma X, Zhu X, Gong S, Xie X, Hu J, Am K-M, Zhong Y (2017) Person re-identification by unsupervised video matching. Pattern Recogn 65:197–210

    Article  Google Scholar 

  31. N Martinel, C Micheloni, and G Feresti (2014). “Saliency weighted features for person re-identification”, In ECCV Workshop on Visual Surveillance and Re-identification

  32. Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, and Yoichi Sato (2016). “Hierarchical gaussian descriptor for person re-identification”,In CVPR

  33. MichelaFarenzena, Loris Bazzani, Alessandro Perina, Vittorio Murino, and Marco Cristani (2010). “Person re-identification by symmetry-driven accumulation of local features”, In CVPR

  34. OM Parkhi, A Vedald, and A Zisserman (2015). “Deep face recognition,” in Proceedings of the British machine vision conference

  35. S Pedagadi, J Orwell, S Velastin, and B Boghossian (2013). “Local fisher discriminant analysis for pedestrian re-identification”, In CVPR, pp. 3318–3325

  36. P Peng, T Xiang, Y Wang, M Pontil, S Gong, T Huang, and Y Tian (2016). “Unsupervised cross-dataset transfer learning for person re-identification”

  37. F Schroff, K Dmitry, and P James (2015). “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 815–823

  38. Arulkumar Subramaniam, Moitreya Chatterjee, and Anurag Mittal (2016). “Deep neural networks with inexact matching for person re-identification”, In NIPS

  39. Dapeng Tao, Yanan Guo, Baosheng Yu, Jianxin Pang and Zhengtao Yu (2017). “Deep multi-view feature learning for person re-identification”, IEEE Transactions on Circuits and Systems for Video Technology, pp.1–19

  40. Varior RR, Wang G, Lu J, Liu T (2016) Learning invariant color features for person reidentification. IEEE Trans Image Process 25(7):3395–3410

    Article  MathSciNet  Google Scholar 

  41. VenkataSubramanyam VG, Ahuja R (2019) Robust discriminative subspace learning for person re-identification. IEEE Signal Processing Letters 26(1):154–158

    Article  Google Scholar 

  42. Taiqing Wang, Shaogang Gong,Xiatian Zhu, and Shengjin Wang (2014). “Person re-identification by video ranking”, In ECCV

  43. X Wang, WS Zheng, X Li, and J Zhang (2015). “Cross-scenario transfer person re-identification”, IEEE Transactions on Circuits and Systems for Video Technology

  44. L Wu, C Shen, and A van den Hengel (2016). “Personnet: Person re-identification with deep convolutional neural networks”, arXiv preprint arXiv:1601.07255

  45. T Xiao, H Li, W Ouyang, and X Wang (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

  46. T Xiao, S Li, B Wang, L Lin, and X Wang (2016). “End-to-end deep learning for person search”, proceedings of the 14th European conference on computer vision (ECCV)

  47. F Xiong, M Gou, O Camps, and M. Sznaier (2014). “Person re-identification using kernel-based metric learning methods”, In ECCV, pp. 1–16

  48. Y Yang, J Yang, J Yan, S Liao, D Yi, and SZ Li (2014). “Salient color names for person re-identification”, In ECCV, pp. 536–551

  49. D Yi, Z Lei, S Liao, SZ Li et al. (2014). “Deep metric learning for person re-identification.” in ICPR, vol. 2014, pp. 34–39

  50. R Zhao, W Ouyang, and X Wang (2013). “Person re-identification by salience matching”, In ICCV, pp.2528–2535

  51. R Zhao, W Ouyang, and X Wang (2013). “Unsupervised salience learning for person re-identification”, In CVPR, pp. 3586–3593

  52. WS Zheng, S Gong, and T Xiang (2011). “Person re-identification by probabilistic relative distance comparison”, In CVPR, pp. 649–656

  53. Zheng WS, Gong S, Xiang T (2012) Re-identification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668

    Article  Google Scholar 

  54. L Zheng, L Shen, L Tian, S Wang, J Wang, and Q Tian (2015). “Scalable person re-identification: a benchmark”, In ICCV, pp. 1116–1124

Download references

Author information

Authors and Affiliations

  1. Vin solutions, Tirunelveli, India

    K. Jayapriya

  2. Department of CSE, GITAM University, Bangalore, India

    I. Jeena Jacob

  3. SarahTucker College, Tirunelveli, India

    N. Ani Brown Mary

Authors
  1. K. Jayapriya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Jeena Jacob
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Ani Brown Mary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Jayapriya.

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

Jayapriya, K., Jacob, I.J. & Mary, N.A.B. Person re-identification using prioritized chromatic texture (PCT) with deep learning. Multimed Tools Appl 79, 29399–29410 (2020). https://doi.org/10.1007/s11042-020-09528-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09528-8

Keywords

Search

Navigation