nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Temporal Model Adaptation for Person Re-identification

verfasst von : Niki Martinel, Abir Das, Christian Micheloni, Amit K. Roy-Chowdhury

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about \(80\,\%\).

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

Nur mit Berechtigung zugänglich

Effect of this initialization is checked by adding random noise to each element of \(\mathbf {h}\). Results show that in 96 % of the cases the output cluster is the same.

See supplementary for additional results on the 3DPeS and CUHK03 datasets.

http://www.liangzheng.com.cn.

The percentage of labeled pairs is computed with respect to n.

Vezzani, R., Baltieri, D., Cucchiara, R.: People reidentification in surveillance and forensics. ACM Comput. Surv. 46(2), 1–37 (2013)CrossRef

Wu, Z., Li, Y., Radke, R.J.: Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features. IEEE Trans. Pattern Anal. Mach. Intell. 37(5), 1095–1108 (2015)CrossRef

Lisanti, G., Masi, I., Bagdanov, A.D., Bimbo, A.D.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1629–1642 (2015)CrossRef

Martinel, N., Micheloni, C.: Sparse matching of random patches for person re-identification. In: International Conference on Distributed Smart Cameras, pp. 1–6 (2014)

Xiong, F., Gou, M., Camps, O., Sznaier, M.: Using kernel-based metric learning methods. In: European Conference Computer Vision, pp. 1–16 (2014)

Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: International Conference on Computer Vision and Pattern Recognition (2015)

Paisitkriangkrai, S., Shen, C., Hengel, A.V.D.: Learning to rank in person re-identification with metric ensembles. In: International Conference on Computer Vision and Pattern Recognition (2015)

Liu, C., Loy, C.C., Gong, S., Wang, G.: POP: person re-identification post-rank optimisation. In: International Conference on Computer Vision (2013)

Chen, D., Yuan, Z., Hua, G., Zheng, N., Wang, J.: Similarity learning on an explicit polynomial kernel feature map for person re-identification. In: International Conference on Computer Vision and Pattern Recognition (2015)

10.

Garcia, J., Martinel, N., Micheloni, C., Gardel, A.: Person re-identification ranking optimisation by discriminant context information analysis. In: International Conference on Computer Vision (2015)

11.

Boyd, S., Parikh, N., E Chu, B.P., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2010)CrossRefMATH

12.

Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 167–172 (2007)CrossRef

13.

Zheng, W.S., Li, X., Xiang, T., Liao, S., Lai, J., Gong, S.: Partial person re-identification. In: International Conference on Computer Vision, pp. 4678–4686 (2015)

14.

Li, X., Zheng, W.S., Wang, X., Xiang, T., Gong, S.: Multi-scale learning for low-resolution person re-identification. In: International Conference on Computer Vision, pp. 3765–3773 (2015)

15.

Martinel, N., Foresti, G.L., Micheloni, C.: Person reidentification in a distributed camera network framework. IEEE Trans. Cybern. 1–12 (in press, 2016)

16.

Zheng, W.S., Gong, S., Xiang, T.: Towards open-world person re-identification by one-shot group-based verification. IEEE Trans. Pattern Anal. Mach. Intell. 8828(2), 1–1 (2015)

17.

Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. Adv. Neural Inf. Process. Syst. 15, 505–512 (2002)

18.

Bellet, A., Habrard, A., Sebban, M.: A Survey on Metric Learning for Feature Vectors and Structured Data. ArXiv e-prints, June 2013

19.

Hirzer, M., Roth, P.M., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 780–793. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_56

20.

Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: International Conference on Computer Vision, pp. 3685–3693 (2015)

21.

Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: International Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)

22.

Tao, D., Jin, L., Wang, Y., Yuan, Y., Li, X.: Person re-identification by regularized smoothing KISS metric learning. IEEE Trans. Circ. Syst. Video Technol. 23(10), 1675–1685 (2013)CrossRef

23.

Tao, D., Jin, L., Wang, Y., Li, X.: Person reidentification by minimum classification error-based KISS metric learning. IEEE Trans. Cyber. 45(2), 1–11 (2014)

24.

Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: International Conference on Computer Vision and Pattern Recognition, pp. 3610–3617. IEEE, June 2013

25.

Pedagadi, S., Orwell, J., Velastin, S.: Local fisher discriminant analysis for pedestrian re-identification. In: International Conference on Computer Vision and Pattern Recognition, pp. 3318–3325 (2013)

26.

Martinel, N., Micheloni, C.: Classification of local eigen-dissimilarities for person re-identification. ieee sig. process. lett. 22(4), 455–459 (2015)CrossRef

27.

García, J., Martinel, N., Gardel, A., Bravo, I., Foresti, G.L., Micheloni, C.: Modeling feature distances by orientation driven classifiers for person re-identification. J. Vis. Commun. Image Representation 38, 115–129 (2016)CrossRef

28.

Martinel, N., Micheloni, C., Foresti, G.L.: Kernelized saliency-based person re-identification through multiple metric learning. IEEE Trans. Image Process. 24(12), 5645–5658 (2015)MathSciNetCrossRef

29.

Chen, J., Zhang, Z., Wang, Y.: Relevance metric learning for person re-identification by exploiting listwise similarities. IEEE Trans. Image Process. 7149(c), 1–1 (2015)MathSciNet

30.

Zheng, W.S., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRef

31.

Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: Conference on Computer Vision and Pattern Recognition, pp. 152–159, June 2014

32.

Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: IEEE International Conference on Computer Vision and Pattern Recognition (2015)

33.

Zhang, R., Lin, L., Zhang, R., Zuo, W., Zhang, L.: Bit-Scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)MathSciNetCrossRef

34.

Settles, B.: Active learning. Synth. Lect. Artif. Intell. Mach. Learn. 6(1), 1–114 (2012)MathSciNetCrossRefMATH

35.

Joshi, A.J., Porikli, F., Papanikolopoulos, N.P.: Scalable active learning for multiclass image classification. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2259–2273 (2012)CrossRef

36.

Aodha, O.M., Campbell, N.D.F., Kautz, J., Brostow, G.J.: Hierarchical subquery evaluation for active learning on a graph. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

37.

Biswas, A., Parikh, D.: Simultaneous active learning of classifiers & attributes via relative feedback. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)

38.

Chakraborty, S., Balasubramanian, V.N., Panchanathan, S.: Optimal batch selection for active learning in multi-label classification. In: ACM International Conference on Multimedia, pp. 1413–1416 (2011)

39.

Elhamifar, E., Sapiro, G., Yang, A., Sasrty, S.S.: A convex optimization framework for active learning. In: IEEE International Conference on Computer Vision, pp. 209–216 (2013)

40.

Vondrick, C., Ramanan, D.: Video annotation and tracking with active learning. In: Advances in Neural Information Processing Systems (2011)

41.

Vijayanarasimhan, S., Grauman, K.: Large-scale live active learning: training object detectors with crawled data and crowds. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)

42.

Vezhnevets, A., Buhmann, J.M., Ferrari, V.: Active learning for semantic segmentation with expected change. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3162–3169 (2012)

43.

Karasev, V., Ravichandran, A., Soatto, S.: Active frame, location, and detector selection for automated and manual video annotation. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

44.

Hasan, M., Roy-Chowdhury, A.K.: Context aware active learning of activity recognition models. In: IEEE International Conference on Computer Vision (2015)

45.

Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K.: Region-based interactive ranking optimization for person re-identification. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 1–10. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13168-9_1

46.

Das, A., Panda, R., Roy-Chowdhury, A.: Active image pair selection for continuous person re-identification. In: International Conference on Image Processing (2015)

47.

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

48.

Guo, Z.C., Ying, Y.: Guaranteed classification via regularized similarity learning. Neural Comput. 26(3), 497–522 (2013)MathSciNetCrossRef

49.

Xia, H., Hoi, S.C.H., Jin, R., Zhao, P.: Online multiple kernel similarity learning for visual search. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 536–549 (2014)CrossRef

50.

Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint L2, 1-norms minimization. In: Advances in Neural Information Processing Systems, pp. 1813–1821 (2010)

51.

Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Convex optimization with sparsity-inducing norms. In: Sra, S., Nowozin, S., Wright, S.J. (eds.) Optimization for Machine Learning, pp. 1–35. The MIT Press (2011)

52.

Cao, X., Zhang, H., Guo, X., Liu, S., Chen, X.: Image retrieval and ranking via consistently reconstructing multi-attribute queries. In: European Conference on Computer Vision, pp. 569–583 (2014)

53.

Zhang, C., Fu, H., Liu, S., Liu, G., Cao, X.: Low-Rank tensor constrained multiview subspace clustering. In: International Conference on Computer Vision, pp. 1582–1590 (2015)

54.

Ouyang, H., He, N., Tran, L., Gray, A.: Stochastic alternating direction method of multipliers. In: International Conference on Machine Learning, pp. 80–88 (2013)

55.

Zhao, S.Y., Li, W.J., Zhou, Z.H.: Scalable stochastic alternating direction method of multipliers. arXiv preprint arXiv:1502.03529 (2015)

56.

Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. Adv. Neural Inf. Process. Syst. 1(3), 1–9 (2013)

57.

Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A.J., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in large margin classifiers, pp. 61–74. The MIT Press (1999)

58.

Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recongnition, reacquisition and tracking. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Rio De Janeiro, Brazil, October 2007

59.

Roth, P.M., Hirzer, M., Koestinger, M., Beleznai, C., Bischof, H.: Mahalanobis distance learning for person re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification, pp. 247–267 (2014)

60.

Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: International Conference on Computer Vision (2015)

61.

Yang, Y., Jimei, Y., Junjie, Y., Liao, S.: Salient color names for person re-identification. In: European Conference on Computer Vision (2014)

62.

Shen, Y., Lin, W., Yan, J., Xu, M., Wu, J., Wang, J.: Person re-identification with correspondence structure learning. In: International Conference on Computer Vision, pp. 3200–3208 (2015)

63.

Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: International Conference on Computer Vision and Pattern Recognition, pp. 144–151. IEEE, June 2014

64.

Martinel, N., Das, A., Micheloni, C., Roy-Chowdhury, A.K.: Re-identification in the function space of feature warps. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1656–1669 (2015)CrossRef

65.

Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: International Conference on Machine Learning, pp. 209–216. ACM Press, New York (2007)

66.

Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)MATH

67.

Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: International Conference on Computer Vision, pp. 498–505. IEEE, September 2009

Titel: Temporal Model Adaptation for Person Re-identification
verfasst von: Niki Martinel
Abir Das
Christian Micheloni
Amit K. Roy-Chowdhury
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46492-3

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

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46493-0_52

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"