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Person re-identification based on multi-scale convolutional network

  • XiuJie YangEmail author
  • Ping Chen
Article
  • 11 Downloads

Abstract

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recently, with the success of deep learning methods on many computer vision tasks, researchers started to put their focuses on learning high-performance features. In this paper, we propose a method by fusing features learned from a multi-scale convolutional neural network and the traditional hand-crafted features, which improves the performance significantly. The Shinpuhkan2014dataset has been selected as the training data, and we compare the proposed method on VIPeR, PRID, iLIDS and CUHK03 datasets. The experimental results show that the performance of the proposed method is superior to the current methods which have a training step on the testing sets.

Keywords

Person re-identification Evaluation methods Feature representation Real application 

Notes

Acknowledgements

This work is supported by Scientific Research Project of Chongqing Education Commission No. KJ1729408.

References

  1. 1.
    Bazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by hpe signature. In: 2010 20th international conference on pattern recognition (ICPR). IEEE, pp 1413–1416Google Scholar
  2. 2.
    Cheng D S, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification. In: BMVC, vol 2, p 6Google Scholar
  3. 3.
    Davis J V, Kulis B, Jain P, Sra S, Dhillon I S (2007) Information-theoretic metric learning. In: Proceedings of the 24th international conference on machine learning. ACM, pp 209–216Google Scholar
  4. 4.
    Dikmen M, Akbas E, Huang T S, Ahuja N (2011) Pedestrian recognition with a learned metric. In: Computer vision–ACCV 2010. Springer, pp 501–512Google Scholar
  5. 5.
    Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR, pp 2360–2367Google Scholar
  6. 6.
    Gheissari N, Sebastian T B, Hartley R (2006) Person reidentification using spatiotemporal appearance. In: CVPR (2), pp 1528–1535Google Scholar
  7. 7.
    Globerson A, Roweis S T (2005) Metric learning by collapsing classes. In: NIPSGoogle Scholar
  8. 8.
    Gong S, Cristani M, Yan S, Loy C C (2014) Person re-identification. Springer, BerlinCrossRefGoogle Scholar
  9. 9.
    Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: ECCV (1), pp 262–275Google Scholar
  10. 10.
    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. CiteseerGoogle Scholar
  11. 11.
    Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 498–505Google Scholar
  12. 12.
    Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008) Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: ICDSC, pp 1–6Google Scholar
  13. 13.
    Hirzer M, Beleznai C, Roth P M, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Proceedings of the 17th Scandinavian conference on image analysis, ser. SCIA’11. [Online]. Available: http://dl.acm.org/citation.cfm?id=2009594.2009606. Springer, Berlin, pp 91–102
  14. 14.
    Hu Y, Liao S, Lei Z, Yi D, Li S Z (2013) Exploring structural information and fusing multiple features for person re-identification. In: 2013 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 794–799Google Scholar
  15. 15.
    Kostinger M, Hirzer M, Wohlhart P, Roth P M, Bischof H (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2288–2295Google Scholar
  16. 16.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  17. 17.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1106–1114Google Scholar
  18. 18.
    Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278–2324Google Scholar
  19. 19.
    Li W, Wang X (2013) Locally aligned feature transforms across views. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3594–3601Google Scholar
  20. 20.
    Li Z, Chang S, Liang F, Huang T S, Cao L, Smith J R (2013) Learning locally-adaptive decision functions for person verification. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3610–3617Google Scholar
  21. 21.
    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. IEEE, pp 152–159Google Scholar
  22. 22.
    Liao S, 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. IEEE, pp 2197–2206Google Scholar
  23. 23.
    Liu Y, Shao Y, Sun F (2012) Person re-identification based on visual saliency. In: 2012 12th international conference on intelligent systems design and applications (ISDA). IEEE, pp 884–889Google Scholar
  24. 24.
    Ma B, Su Y, Jurie F (2012) Local descriptors encoded by fisher vectors for person re-identification. In: Computer vision–ECCV 2012. Workshops and Demonstrations. Springer, pp 413–422Google Scholar
  25. 25.
    Ma B, Su Y, Jurie F et al (2012) Bicov: a novel image representation for person re-identification and face verification. In: British Machive vision conferenceGoogle Scholar
  26. 26.
    Ma A, Yuen P, Li J (2013) Domain transfer support vector ranking for person re-identification without target camera label information. In: 2013 IEEE international conference on computer vision (ICCV), pp 3567–3574Google Scholar
  27. 27.
    Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML, pp 807–814Google Scholar
  28. 28.
    Prosser B, Zheng W-S, Gong S, Xiang T, Mary Q (2010) Person re-identification by support vector ranking. In: BMVC, vol 1, p 5Google Scholar
  29. 29.
    Simpson D G (1987) Minimum hellinger distance estimation for the analysis of count data. J Am Stat Assoc 82(399):802–807MathSciNetCrossRefGoogle Scholar
  30. 30.
    Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708Google Scholar
  31. 31.
    Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: Conference on computer vision and pattern recognition (CVPR)Google Scholar
  32. 32.
    Varior R R, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision, pp 791–808Google Scholar
  33. 33.
    Wang X, Doretto G, Sebastian T, Rittscher J, Tu P H (2007) Shape and appearance context modeling. In: ICCV, pp 1–8Google Scholar
  34. 34.
    Wang F, Zuo W, Lin L, Zhang D, Zhang L (2016) Joint learning of single-image and cross-image representations for person reidentification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 1288–1296Google Scholar
  35. 35.
    Weinberger K, Blitzer J, Saul L (2006) Distance metric learning for large margin nearest neighbor classification, vol 18, p 1473Google Scholar
  36. 36.
    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 1249–1258Google Scholar
  37. 37.
    Xing E P, Ng A Y, Jordan M I, Russell SJ (2002) Distance metric learning with application to clustering with side-information. In: NIPS, pp 505–512Google Scholar
  38. 38.
    Yi D, Lei Z, Liao S, Li S Z (2014) Deep metric learning for person re-identification. In: International conference on pattern recognitionGoogle Scholar
  39. 39.
    Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. arXiv preprint arXiv:1603.02139
  40. 40.
    Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3586–3593Google Scholar
  41. 41.
    Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: 2013 IEEE conference on international conference on computer vision (ICCV). IEEE, pp 2528–2535Google Scholar
  42. 42.
    Zheng W-S, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: CVPR, pp 649–656Google Scholar
  43. 43.
    Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision. IEEE, pp 1116–1124Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chongqing College of Electronic Engineering of Digital Media CollegeChongqingChina
  2. 2.Chongqing College of Electronic Engineering of General Education and International CollegeChongqingChina

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