Skip to main content
SpringerLink
Log in

Deep learning with particle filter for person re-identification

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

Abstract

Person re-identification, having attracted much attention in the multimedia community, is still challenged by the accuracy and the robustness, as the images for the verification contain such variations as light, pose, noise and ambiguity etc. Such practical challenges require relatively robust and accurate feature learning technologies. We introduced a novel deep neural network with PF-BP(Particle Filter-Back Propagation) to achieve relatively global and robust performances of person re-identification. The local optima in the deep networks themselves are still the main difficulty in the learning, in despite of several advanced approaches. A novel neural network learning, or PF-BP, was first proposed to solve the local optima problem in the non-convex objective function of the deep networks. When considering final deep network to learn using BP, the overall neural network with the particle filter will behave as the PF-BP neural network. Also, a max-min value searching was proposed by considering two assumptions about shapes of the non-convex objective function to learn on. Finally, a salience learning based on the deep neural network with PF-BP was proposed to achieve an advanced person re-identification. We test our neural network learning with particle filter aimed to the non-convex optimization problem, and then evaluate the performances of the proposed system in a person re-identification scenario. Experimental results demonstrate that the corresponding performances of the proposed deep network have promising discriminative capability in comparison with other ones.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Bengio Y (2009) Learning deep architectures for AI. In: Foundations and Trends in Machine Learning, vol 2, pp 1–127

    Article  MathSciNet  Google Scholar 

  2. Bottou L, LeCun Y (2004) Large scale online learning. Proceedings NIPS, 2004

  3. Choe G et al (2016) An advanced association of particle filtering and kernel based object tracking. Multimedia Tools and Applications, 74(18)

    Article  Google Scholar 

  4. Choe G et al (2016) Combined Salience based Person Re-identification. Multimedia Tools and Applications, 75(18)

    Article  Google Scholar 

  5. Cui J et al (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans. on Systems, Man and Cybernetics 43(4)

    Article  Google Scholar 

  6. Dahl G et al (2010) Phone recognition with the mean-covariance restricted Boltzmann machine. Proc NIPS 23:469–477

    Google Scholar 

  7. Dahl G et al (2011) Context-dependent DBN-HMMs in large vocabulary continuous speech recognition. Proceedings ICASSP

  8. Dahl G et al (2012) Context-dependent, pre-trained deep neural networks for large voca-bulary speech recognition. IEEE Trans Audio Speech Language Proc 20(1):30–42

    Article  Google Scholar 

  9. Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn 18:2993–3003

    Article  Google Scholar 

  10. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR. IEEE, pp 2360-2367

  11. Gao Y et al (2017) Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans. on Image Processing 26(5)

    Article  MathSciNet  Google Scholar 

  12. Gray D, Tao H (2008) Viewpoint in variant pedestrian recognition with an ensemble of localized features. In: ECCV. Springer, pp 262-275

  13. Hinton G et al (2012) Deep Neural Networks for Acoustic Modeling in Speech Recogni-tion. IEEE Signal Proc Mag 29(6):82–97

    Article  Google Scholar 

  14. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1097-1105

  15. Layne R, Hospedales T, Gong S (2012) Towards person identification and re-identification with attributes. In: ECCV. Springer, pp 402-412

  16. Li W, Zhao R, Wang X (2013) Human re-identification with transferred metric learning. In: ACCV. Springer, pp 31-44

  17. Li W, Zhao R, Xiao T, Wang X (2014) Deep reid:Deep filter pairing neural network for person re-identification. In: CVPR, pp 152-159

  18. Li Y et al (2018) Large-scale remote sensing image retrieval by deep hashing neural networks. IEEE Trans. on Geoscience and Remote Sensing 56(2)

    Article  Google Scholar 

  19. Li Z, Chang S, Liang F, Huang T, Cao L, Smith J (2013) Learning locally-adaptive decision functions for person verification. In: CVPR. IEEE, pp 3610-3617

  20. Li Z et al (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans. on Image Processing 17(11)

    Article  Google Scholar 

  21. Li Z et al (2017) Weakly supervised deep matrix factorization for social image understanding. IEEE Trans. on Image Processing 26(1)

    Article  MathSciNet  Google Scholar 

  22. Lin L, Liu X, Zhu S (2010) Layered graph matching with composite cluster sampling. IEEE Trans Pattern Anal Mach Intell 32(8):1426–1442

    Article  Google Scholar 

  23. Lin L, Wu T, Porway J, Xu Z (2009) A stochastic graph grammar for compositional object representation and recognition. Pattern Recogn 42(7):1297–1307

    Article  Google Scholar 

  24. Lin L, Zhang R, Duan X (2015) Adaptive scene category discovery with generative learning and compositional sampling. IEEE Trans Circuits Syst Video Technol 25(2):251–260

    Article  Google Scholar 

  25. Liu H, Ma B, Qin L, Pang J, Zhang C, Huang Q (2015) Set-label modeling anddeep metric learning on person re-identification. Neurocomputing 151:1283–1292

    Article  Google Scholar 

  26. Liu L et al (2016) Recognizing complex activities by a probabilistic interval-based model. National Conference on Artificial Intelligence(AAAI)

  27. Liu W et al (2017) Multiview dimension reduction via Hessian multiset canonical correlations. Information Fusion. https://doi.org/10.1016/j.inffus.2017.09.001

    Article  Google Scholar 

  28. Liu Y et al (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. ICPR pp 898–901

  29. Liu Y et al (2016) From action to activity: sensor-baesd activity recognition. Neurocomputing 181(12)

    Article  Google Scholar 

  30. Ma J et al (2015) Non-rigid visible and infrared face registration via regularized Gaussian fields criterion. Pattern Recogn 48(3)

    Article  Google Scholar 

  31. Martens J (2010) Deep learning with Hessian-free optimization. Proceedings ICML

  32. Mohamed A et al (2010) Investigation of full-sequence training of deep belief networks for speech recognition. Proc. Interspeech

  33. Mohamed A et al (2012) Acoustic Modeling Using Deep Belief Networks. IEEE Trans. Audio, Speech, Language Proc. 20(1)

    Article  Google Scholar 

  34. Ranzato M, Boureau Y, LeCun Y (2007) Sparse Feature Learning for Deep Belief Networks, Proceedings NIPS

  35. Rifai S et al (2011) Contractive autoencoders: Explicit invariance during feature extrac-tion. Proceedings ICML, pp 833–840

  36. Seide F et al (2011) Feature engineering in context-dependent deep neural networks for conversational speech transcription. Proceedings ASRU, pp 24–29

  37. Seide F, Li G, Yu D (2011) Conversational Speech Transcription Using Context-Dependent Deep Neural Networks. Interspeech, pp 437–440

  38. Vincent P et al (2010) Stacked denoising autoencoders: Leaning useful representations in a deep network with a local denoising criterion. J Machine Learning Research 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  39. Vinyals O, Povey D (2012) Krylov Subspace Descent for Deep Learning. Proceedings AISTAT

  40. Wang J, Leung T, Rosenberg C, Wang J, Philbin J, Chen B, Wu Y et al (2014) Learning fine-grained image similarity with deep ranking. In: CVPR

  41. Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: ICCV. IEEE, pp 1-8

  42. Wang Z et al (2016) Zero-shot person re-identification via cross-view consistency. IEEE Trans. on Multimedia 18(2)

    Article  Google Scholar 

  43. Wang Z et al (2017) Statistical inference of Gaussian-Laplace distribution for person verification. ACM Multimedia(MM) pp 1607–1617

  44. Yang X et al (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385-386:338–352

    Article  Google Scholar 

  45. Yu D, Deng L, Li G, Seide F (2011) Discriminative pre-training of deep neural networks U.S. Patent Filing

  46. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: CVPR. pp 4321–4328

  47. Zhao Z et al (2017) Remarkable local resampling based on particle filter for visual tracking. Multimedia Tools and Applications, 76(1)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Visual Information Processing Laboratory, School of Computer Science and Technology, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Gwangmin Choe, Chunhwa Choe & Hyoson So

  2. Intelligent and Distributed Computing Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Tianjiang Wang & Caihong Yuan

  3. Information and Communication Laboratory, School of Computer Science and Technology, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Cholman Nam

Authors
  1. Gwangmin Choe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunhwa Choe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianjiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyoson So
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cholman Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Caihong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Gwangmin Choe or Tianjiang Wang.

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

Choe, G., Choe, C., Wang, T. et al. Deep learning with particle filter for person re-identification. Multimed Tools Appl 78, 6607–6636 (2019). https://doi.org/10.1007/s11042-018-6415-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6415-5

Keywords

Search

Navigation