Advertisement

Human Reidentification with Transferred Metric Learning

  • Wei Li
  • Rui Zhao
  • Xiaogang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)

Abstract

Human reidentification is to match persons observed in non-overlapping camera views with visual features for inter-camera tracking. The ambiguity increases with the number of candidates to be distinguished. Simple temporal reasoning can simplify the problem by pruning the candidate set to be matched. Existing approaches adopt a fixed metric for matching all the subjects. Our approach is motivated by the insight that different visual metrics should be optimally learned for different candidate sets. We tackle this problem under a transfer learning framework. Given a large training set, the training samples are selected and reweighted according to their visual similarities with the query sample and its candidate set. A weighted maximum margin metric is online learned and transferred from a generic metric to a candidate-set-specific metric. The whole online reweighting and learning process takes less than two seconds per candidate set. Experiments on the VIPeR dataset and our dataset show that the proposed transferred metric learning significantly outperforms directly matching visual features or using a single generic metric learned from the whole training set.

Keywords

Training Sample Visual Feature Camera View Transfer Learning Temporal Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gheissari, N., Sebastian, T.B., Rittscher, J., Hartley, R.: Person reidentification using spatiotemporal appearance. In: CVPR (2006)Google Scholar
  2. 2.
    Schwartz, W., Davis, L.: Learning discriminative appearance-based models using partial least sqaures. In: Proc. XXII SIBGRAPI (2009)Google Scholar
  3. 3.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: CVPR (2010)Google Scholar
  4. 4.
    Zheng, W., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR (2011)Google Scholar
  5. 5.
    Park, U., Jain, A., Kitahara, I., Kogure, K., Hagita, N.: Vise: Visual search engine using multiple networked cameras. In: ICPR (2006)Google Scholar
  6. 6.
    van de Weijer, J., Schmid, C.: Coloring Local Feature Extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  8. 8.
    Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: ICCV (2007)Google Scholar
  9. 9.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A 2, 1160–1169 (1985)CrossRefGoogle Scholar
  10. 10.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on PAMI, 971–987 (2002)Google Scholar
  11. 11.
    Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: ICCV (2003)Google Scholar
  12. 12.
    Porikli, F.: Inter-camera color calibration by correlation model function. In: ICIP (2003)Google Scholar
  13. 13.
    Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: CVPR (2005)Google Scholar
  14. 14.
    Gilbert, A., Bowden, R.: Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 125–136. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Prosser, B., Gong, S., Xiang, T.: Multi-camera matching using bi-directional cumulative brightness transfer function. In: BMVC (2008)Google Scholar
  16. 16.
    Gray, D., Tao, H.: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Shan, Y., Sawhney, H.S., Kumar, R.: Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Nonoverlapping Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 700–711 (2008)CrossRefGoogle Scholar
  18. 18.
    Lin, Z., Davis, L.S.: Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 23–34. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Prosser, B., Zheng, W., Gong, S., Xiang, T., Mary, Q.: Person re-identification by support vector ranking. In: BMVC (2010)Google Scholar
  20. 20.
    Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6, 1453–1484 (2005)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition and tracking (2007)Google Scholar
  22. 22.
    Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proc. of ICML (2007)Google Scholar
  23. 23.
    Wu, X., Srihari, R.: Incorporating prior knowledge with weighted margin support vector machines. In: Proc. of SIGKDD (2004)Google Scholar
  24. 24.
    Jiang, W., Zavesky, E., Chang, S., Loui, A.: Cross-domain learning methods for high-level visual concept classification. In: ICIP (2008)Google Scholar
  25. 25.
    Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting Visual Category Models to New Domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: CVPR (2010)Google Scholar
  27. 27.
    Qi, G., Aggarwal, C., Huang, T.: Towards semantic knowledge propagation from text corpus to web images. In: Proc. of WWW (2011)Google Scholar
  28. 28.
    Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive svms. In: Proc. of ACM Multimedia (2007)Google Scholar
  29. 29.
    Duan, L., Tsang, I.W., Xu, D., Maybank, S.J.: Domain transfer svm for video concept detection. In: CVPR (2009)Google Scholar
  30. 30.
    Qi, G., Aggarwal, C., Rui, Y., Tian, Q., Chang, S., Huang, T.: Towards cross-category knowledge propagation for learning visual concepts. In: CVPR (2011)Google Scholar
  31. 31.
    Zhan, D.C., Li, M., Li, Y.F., Zhou, Z.H.: Learning instance specific distances using metric propagation. In: Proc. of ICML, p. 154 (2009)Google Scholar
  32. 32.
    Sande, K., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. on PAMI 32, 1582–1596 (2010)CrossRefGoogle Scholar
  33. 33.
    Liu, T., Moore, A.W., Gray, A.G., Yang, K.: An investigation of practical approximate nearest neighbor algorithms. In: Proc. of NIPS (2004)Google Scholar
  34. 34.
    Fletcher, R.: Semi-definite matrix constraints in optimization. SIAM J. Control Optim. 23, 493–513 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin distance metric learning for large margin. Journal of Machine Learning Research 10, 207–244 (2009)zbMATHGoogle Scholar
  36. 36.
    Luenberger, D.G., Ye, Y.: Linear and Nonlinear Programming. Springer (2008)Google Scholar
  37. 37.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3, 1–122 (2011)CrossRefGoogle Scholar
  38. 38.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proc. of ICML (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wei Li
    • 1
  • Rui Zhao
    • 1
  • Xiaogang Wang
    • 1
  1. 1.Electronic Engineering DepartmentThe Chinese University of Hong KongHong Kong

Personalised recommendations