Skip to main content
SpringerLink
Log in

A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

Person re-identification is a challenging problem in computer vision. Lots of research interest is observed in this area over the past few years. A model for complete person re-identification can prove useful in this direction. Use of convolutional neural networks for pedestrian detection can improve the accuracy of detection to a larger extent. Deriving a descriptor which is invariant to the changes in the illumination, background and the pose can make the difference in the re-identification process. The predominant part of our work focuses on building a robust descriptor which can tackle such challenges. We have concentrated on building a descriptor by employing appearance-based features extracted both at local and global levels. Further, the dimensionality of the descriptor is reduced using kernel PCA. Distance metric learning algorithms are used to evaluate the descriptor on three major benchmark datasets. We propose a complete person re-identification system which involves both pedestrian detection and person re-identification. Major contributions of this work are to detect pedestrians from surveillance videos using CNN-based learning and to generate a kernel-PCA-based spatial descriptor and evaluate the descriptor using known distance metric learning methods on benchmark datasets.

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. Papageorgiou CP, Oren M, Poggio T (1998) A general framework for object detection. In: Sixth international conference on computer vision, pp 555–562

  2. Viola P, Jones M, Snow D (2003) Detecting pedestrians using patterns of motion and appearance. In: International conference on computer vision (ICCV)

  3. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, pp 886–893

  4. Zhu Q, Avidan S, Yeh M, Cheng K (2006) Fast human detection using a cascade of histograms of oriented gradients. In: Proceedings of IEEE conference of computer vision and pattern recognition

  5. Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. In: IEEE conference on computer vision and pattern recognition, pp 1–8

  6. Felzenszwalb PF, McAllester DA, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: IEEE conference on computer vision and pattern recognition, pp 1–8

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

  8. Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: IEEE conference on computer vision and pattern recognition, pp 3626–3633

  9. Ouyang W, Wang X (2013) Joint deep learning for pedestrian detection. In: IEEE international conference on computer vision, pp 2056–2063

  10. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: IEEE conference on computer vision and pattern recognition

  11. Bazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by hpe signature. In: 20th international conference on pattern recognition (ICPR)

  12. Cheng DS, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification. In: British machine vision conference

  13. Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. In: Proceedings of 7th IEEE international conference on advanced video and signal based surveillance (AVSS)

  14. Štruc V, Pavecšić N (2009) Gabor-based Kernel partial-least-squares discrimination features for face recognition. Informatica (Vilnius) 20(1):115–138

    MATH  Google Scholar 

  15. Štruc V, Pavecšić N (2010) The complete Gabor-Fisher classifier for robust face recognition. EURASIP Adv Signal Process 2010:26

    MATH  Google Scholar 

  16. Shengcai L, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197–2206

  17. Bingpeng MA, Su Y, Jurie F (2012) Bicov: a novel image representation for person re-identification and face verification. In: British machine vision conference

  18. Weber M, Buml M, Stiefelhagen R (2011) Part-based clothing segmentation for person retrieval. In: 8th IEEE international conference on advanced video and signal based surveillance, AVSS, pp 361–366

  19. Hirzer M, Roth PM, Bischof H (2012) Person re-identification by efficient metric learning. In: Proceedings of IEEE international conference on advanced video and signal-based surveillance

  20. Ijiri Y, Lao S (2012) Human re-identification through distance metric learning based on jensen-shannon kernel. In: International conference on computer vision theory and applications, pp 603–612

  21. Pedagadi S, Orwell J, Velastin S, Boghossian B (2013) Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3318–3325

  22. Xiang ZJ, Chen Q, Liu Y (2014) Person re-identification by fuzzy space color histogram. Multimed Tools Appl 73(1):91–107

    Article  Google Scholar 

  23. Leng Q, Hu R, Liang C, Wang Y, Chen J (2015) Person re-identification with content and context re-ranking. Multimed Tools Appl 74(17):6989–7014

    Article  Google Scholar 

  24. Bazzani L, Cristani M, Perina A, Murino V (2011) Multipleshot person re-identification by chromatic and epitomic analyses. Pattern Recognit Lett 33(7):898–903

    Article  Google Scholar 

  25. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE international workshop on performance evaluation of tracking and surveillance (PETS)

  26. Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. In: Proceedings of IEEE international conference on computer vision

  27. Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: Proceedings of international conference machine learning

  28. Weinberger KQ, Saul LK (2008) Fast solvers and efficient implementations for distance metric learning. In: Proceedings of international conference on machine learning

  29. Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit 48(10):2993–3003

    Article  Google Scholar 

  30. 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

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

  32. Sathish PK, Balaji S (2017) Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos. Int J Multimed Inf Retr 6:289–294

    Article  Google Scholar 

  33. Kaewtrakulpong P, Bowden R (2001) An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proceedings of 2nd European workshop on advanced video based surveillance systems, AVBS01, video based surveillance systems: computer vision and distributed processing

  34. Welch G, Bishop G (2001) An introduction to the kalman filter. An introduction to the kalman filter. In: ACM SIGGRAPH international conference on computer graphics and interactive techniques

  35. Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and semantic segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158

    Article  Google Scholar 

  36. Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding base line for recognition. In: Proceedings of IEEE conference on computer vision pattern recognition workshops, pp 512–519

  37. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  Google Scholar 

  38. Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Proceedings of NIPS, pp 487–495

  39. Baltieri D, Vezzani R, Cucchiara R (2011) 3DPeS: 3d people dataset for surveillance and forensics. In: Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding

  40. Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV (2012) Cats and Dogs. In: IEEE conference on computer vision and pattern recognition

  41. Wang X, Ma X, Grimson E (2009) Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans Pattern Anal Mach Intell (PAMI) 31:539–555

    Article  Google Scholar 

  42. Lowe D (2004) Distinctive image features from scale invariant features. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  43. Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org

  44. Scholkopf B, Smola A, Muller K-R (1999) Kernel principal component analysis. Advances in Kernel methods: support vector learning. MIT Press, Cambridge, pp 327–352

    Google Scholar 

  45. Roth PM, Hirzer M, Kstinger M, Beleznai C, Bischof H (2014) Mahalanobis distance learning for person re-identification. Springer, Berlin, pp 247–267

    Book  Google Scholar 

  46. Hirzer M, Beleznai Csaba, Roth Peter M, Bischof Horst (2011) Person re-identification by descriptive and discriminative classification. In: Proceedings of Scandinavian conference on image analysis (SCIA)

  47. Zheng WS, Gong S, Xiang T (2009) Associating groups of people. In: British machine vision conference

  48. Jojic N, Perina A, Cristani M, Murino V, Frey B (2009) Stel component analysis: modeling spatial correlations in image class structure. In: IEEE conference on computer vision and pattern recognition, pp 2044–2051

  49. Mignon A, Jurie F (2012) PCCA: a new approach for distance learning from sparse pairwise constraints. In: IEEE conference on computer vision and pattern recognition, pp 2666–2672

  50. K̈ostinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition

  51. Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: IEEE international conference on computer vision, pp 2528–2535

  52. Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: The IEEE conference on computer vision and pattern recognition (CVPR)

  53. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 3586–3593

  54. Li Z, Chang S, Liang F, Huang TS, Cao L, Smith JR (2013) Learning locally-adaptive decision functions for person verification. In: IEEE conference on computer vision and pattern recognition, pp 3610–3617

  55. Karanam S, Li Y, Radke R (2015) Sparse re-id: block sparsity for person re-identification. In: Computer Vision and Pattern Recognition workshop

  56. Karanam S, Li Y, Radke RJ (2015) Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: IEEE international conference on computer vision, pp 4516–4524

  57. You J, Wu A, Li X, Zheng WS (2016) Top-push video-based person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1345–1353

  58. 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 

  59. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of the 10th European conference on computer vision

    Google Scholar 

  60. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211–252

    Article  MathSciNet  Google Scholar 

  61. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE Computer society conference on computer vision and pattern recognition, vol 2, pp 2246–252

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Christ University, Bengaluru, 560074, India

    P. K. Sathish

  2. Centre for Incubation, Innovation, Research and Consultancy, Jyothi Institute of Technology, Bengaluru, India

    S. Balaji

Authors
  1. P. K. Sathish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Balaji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. K. Sathish.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sathish, P.K., Balaji, S. A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors. Int J Multimed Info Retr 7, 221–229 (2018). https://doi.org/10.1007/s13735-018-0153-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-018-0153-3

Keywords

Search

Navigation