Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Generating Visual Explanations Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Human-in-the-Loop Person Re-identification

Authors : Hanxiao Wang, Shaogang Gong, Xiatian Zhu, Tao Xiang

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate. Both assumptions scale poorly to real-world applications when camera network size increases and gallery size becomes large. Human verification of automatic model ranked re-id results becomes inevitable. In this work, a novel human-in-the-loop re-id model based on Human Verification Incremental Learning (HVIL) is formulated which does not require any pre-labelled training data to learn a model, therefore readily scalable to new camera pairs. This HVIL model learns cumulatively from human feedback to provide instant improvement to re-id ranking of each probe on-the-fly enabling the model scalable to large gallery sizes. We further formulate a Regularised Metric Ensemble Learning (RMEL) model to combine a series of incrementally learned HVIL models into a single ensemble model to be used when human feedback becomes unavailable.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Weakly-Supervised Semantic Segmentation Using Motion Cues
next chapter Real-Time Monocular Segmentation and Pose Tracking of Multiple Objects
Footnotes
1
In a large size gallery set, true matches are often scarce (only one-shot) and overwhelmed (appear in low-ranks) by false matches of high-ranks in the rank list.
 
2
No limitation on considering any distance/similarity metrics, either learned or not.
 
3
A different 26,960-dim LOMO feature [11] were used for the published XQDA and MLAPG results [11, 12] shown in Table 3. They were worsened using the 5,138-dim feature [47] adopted in our experiments, not shown here due to space limitation.
 
Literature
1.
Gong, S., Cristani, M., Yan, S., Loy, C.C.: Person Re-identification, vol. 1. Springer, London (2014)CrossRefMATH
2.
Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, United States, June 2012
3.
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, United States, June 2012
4.
Zheng, W.S., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35, 653–668 (2013)CrossRef
5.
Pedagadi, S., Orwell, J., Velastin, S.A., Boghossian, B.A.: Local fisher discriminant analysis for pedestrian re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, United States, June 2013
6.
Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, United States, June 2014
7.
Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by video ranking. In: European Conference on Computer Vision, Zurich, Switzerland, September 2014
8.
Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by discriminative selection in video ranking. IEEE Trans. Pattern Anal. Mach, Intell (2016). doi:10.​1109/​TPAMI.​2016.​2522418. in press
9.
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, United States, June 2014
10.
Xiong, F., Gou, M., Camps, O., Sznaier, M.: Person re-identification using kernel-based metric learning methods. In: European Conference on Computer Vision, Zurich, Switzerland, September 2014
11.
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, United States, June 2015, pp. 2197–2206 (2015)
12.
Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: IEEE International Conference on Computer Vision, December 2015
13.
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (2007)
14.
Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference (2009)
15.
Liu, C., Loy, C.C., Gong, S., Wang, G.: Pop: person re-identification post-rank optimisation. In: IEEE International Conference on Computer Vision, Sydney, Australia, December 2013
16.
Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)
17.
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)CrossRef
18.
Gong, S., Cristani, M., Chen, C.L., Hospedales, T.M.: The re-identification challenge. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. Springer, London (2014)CrossRef
19.
Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48, 2993–3003 (2015)CrossRef
20.
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Learning to rank in person re-identification with metric ensembles. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1846–1855 (2015)
21.
Ustinova, E., Ganin, Y., Lempitsky, V.: Multiregion bilinear convolutional neural networks for person re-identification. arXiv preprint arXiv:​1512.​05300 (2015)
22.
Shi, H., Zhu, X., Liao, S., Lei, Z., Yang, Y., Li, S.Z.: Constrained deep metric learning for person re-identification. arXiv preprint arXiv:​1511.​07545 (2015)
23.
Ahmed, E., Jones, M.J., Marks, T.K.: An improved deep learning architecture for person re-identification. In: CVPR, pp. 3908–3916. IEEE (2015)
24.
Liu, X., Song, M., Tao, D., Zhou, X., Chen, C., Bu, J.: Semi-supervised coupled dictionary learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, United States, June 2014
25.
Kodirov, E., Xiang, T., Gong, S.: Dictionary learning with iterative Laplacian regularisation for unsupervised person re-identification. In: British Machine Vision Conference, Swansea, United Kingdom, September 2015
26.
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, United States, June 2010
27.
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, United States, June 2013
28.
Wang, H., Gong, S., Xiang, T.: Unsupervised learning of generative topic saliency for person re-identification. In: British Machine Vision Conference, Nottingham, United Kingdom, September 2014
29.
Layne, R., Hospedales, T.M., Gong, S.: Domain transfer for person re-identification. In: Workshop of ACM International Conference on Multimedia, Barcelona, Catalunya, Spain, October 2013
30.
Ma, A.J., Yuen, P.C., Li, J.: Domain transfer support vector ranking for person re-identification without target camera label information. In: IEEE International Conference on Computer Vision, Sydney, Australia, December 2013
31.
Wang, X., Zheng, W.S., Li, X., Zhang, J.: Cross-scenario transfer person re-identification. IEEE Trans. Circ. Syst. Video Technol. PP(99), 1447–1460 (2015)
32.
Ma, A.J., Li, J., Yuen, P.C., Li, P.: Cross-domain person reidentification using domain adaptation ranking svms. IEEE Trans. Image Process. 24(5), 1599–1613 (2015)MathSciNetCrossRef
33.
Das, A., Panda, R., Roy-Chowdhury, A.: Active image pair selection for continuous person re-identification. In: IEEE International Conference on Image Processing, Quebec, Canada, September 2015
34.
Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-21227-7_​9 CrossRef
35.
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)
36.
Lim, D., Lanckriet, G.: Efficient learning of mahalanobis metrics for ranking. In: Jebara, T., Xing, E.P. (eds.) International Conference on Machine Learning, pp. 1980–1988 (2014)
37.
Weston, J., Bengio, S., Usunier, N.: Large scale image annotation: Learning to rank with joint word-image embeddings. In: European Conference of Machine Learning (2010)
38.
Usunier, N., Buffoni, D., Gallinari, P.: Ranking with ordered weighted pairwise classification. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 1057–1064. ACM, New York (2009)
39.
Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)MathSciNetMATH
40.
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010)CrossRef
41.
Tsuda, K., Rätsch, G., Warmuth, M.K.: Matrix exponentiated gradient updates for on-line learning and bregman projection. J. Mach. Learn. Res. 6, 995–1018 (2005)MathSciNetMATH
42.
Higham, N.J.: Matrix nearness problems and applications. Department of Mathematics, University of Manchester (1988)
43.
44.
Jain, P., Kulis, B., Dhillon, I.S., Grauman, K.: Online metric learning and fast similarity search. In: Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 2009, pp. 761–768 (2009)
45.
46.
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York, NY, USA (2004)CrossRefMATH
47.
Lisanti, G., Masi, I., Del Bimbo, A.: Matching people across camera views using kernel canonical correlation analysis. In: ACM International Conference on Distributed Smart Cameras, Venice, Italy, November 2014
48.
Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: IEEE International Conference on Computer Vision, Kyoto, Japan, September 2009
49.
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)CrossRefMATH
50.
Lin, W.C., Chen, Z.Y., Ke, S.W., Tsai, C.F., Lin, W.Y.: The effect of low-level image features on pseudo relevance feedback. Neurocomputing 166, 26–37 (2015)CrossRef
51.
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40, 5:1–5:60 (2008)CrossRef
52.
Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., Luo, J.: Efficient manifold ranking for image retrieval. In: ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, July 2011, pp. 525–534 (2011)
Metadata
Title
Human-in-the-Loop Person Re-identification
Authors
Hanxiao Wang
Shaogang Gong
Xiatian Zhu
Tao Xiang
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46492-3

Electronic ISBN: 978-3-319-46493-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46493-0_25

Premium Partner

    Image Credits