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Attributes-Based Re-identification

  • Ryan Layne
  • Timothy M. Hospedales
  • Shaogang Gong
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Automated person re-identification using only visual information from public-space CCTV video is challenging for many reasons, such as poor resolution or challenges involved in dealing with camera calibration. More critically still, the majority of clothing worn in public spaces tends to be non-discriminative and therefore of limited disambiguation value. Most re-identification techniques developed so far have relied on low-level visual-feature matching approaches that aim to return matching gallery detections earlier in the ranked list of results. However, for many applications an initial probe image may not be available, or a low-level feature representation may not be sufficiently invariant to viewing condition changes as well as being discriminative for re-identification. In this chapter, we show how mid-level “semantic attributes” can be computed for person description. We further show how this attribute-based description can be used in synergy with low-level feature descriptions to improve re-identification accuracy when an attribute-centric distance measure is employed. Moreover, we discuss a “zero-shot” scenario in which a visual probe is unavailable but re-identification can still be performed with user-provided semantic attribute description.

Keywords

Mutual Information True Match Camera Network Attribute Detector Attribute Ontology 
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.

Notes

Acknowledgments

The authors shall express their deep gratitude to Colin Lewis of the UK MOD SA(SD) who made this work possible and to Toby Nortcliffe of the UK Home Office CAST for providing human operational insight. We also would like to thank Richard Howarth for his assistance in labelling datasets.

References

  1. 1.
    Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: European Conference on Machine Learning (2004)Google Scholar
  2. 2.
    Avraham, T., Gurvich, I., Lindenbaum, M., Markovitch, S.: Learning implicit transfer for person re-identification. In: European Conference on Computer Vision, First International Workshop on Re-identification, Florence (2012)Google Scholar
  3. 3.
    Bazzani, L., Cristani, M., Perina, A., Murino, V.: Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn. Lett. 33(7), 898–903 (2012)CrossRefGoogle Scholar
  4. 4.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)Google Scholar
  5. 5.
    Berg, T.L., Berg, A.C., Shih, J.: Automatic attribute discovery and characterization from noisy web data. In: European Conference on Computer Vision (2010)Google Scholar
  6. 6.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. In: ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)Google Scholar
  7. 7.
    Chawla, N.V., Bowyer, K.W., Hall, L.O.: SMOTE : synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)Google Scholar
  8. 8.
    Cheng, D., Cristani, M., Stoppa, M., Bazzani, L.: Custom pictorial structures for re-identification. In: British Machine Vision Conference (2011)Google Scholar
  9. 9.
    Dantcheva, A., Velardo, C., Dángelo, A., Dugelay, J.L.: Bag of soft biometrics for person identification. Multimedia Tools Appl. 51(2), 739–777 (2011)CrossRefGoogle Scholar
  10. 10.
    Ferrari, V., Zisserman, A.: Learning visual attributes. In: Neural Information Processing Systems (2007)Google Scholar
  11. 11.
    Fu, Y., Hospedales, T., Xiang, T., Gong, S.: Attribute learning for understanding unstructured social activity. In: European Conference on Computer Vision, Florence (2012)Google Scholar
  12. 12.
    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, vol. 3 (2007)Google Scholar
  13. 13.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, Marseille (2008)Google Scholar
  14. 14.
    He, H., Garcia, E.A.: Learning from imbalanced data. In: IEEE Transactions on Data and Knowledge Engineering, vol. 21 (2009)Google Scholar
  15. 15.
    Hirzer, M., Beleznai, C., Roth, P., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image analysis (2011)Google Scholar
  16. 16.
    Hirzer, M., Roth, P.M., Bischof, H.: Person re-identification by efficient impostor-based metric learning. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance (2012)Google Scholar
  17. 17.
    Hirzer, M., Roth, P.M., Martin, K., Bischof, H., Köstinger, M.: Relaxed pairwise learned metric for person re-identification. In: European Conference on Computer Vision, Florence (2012)Google Scholar
  18. 18.
    Jain, A.K., Dass, S.C., Nandakumar, K.: Soft biometric traits for personal recognition systems. In: International Conference on Biometric Authentication, Hong Kong (2004)Google Scholar
  19. 19.
    Keval, H.: CCTV Control room collaboration and communication: does it Work? In: Human Centred Technology Workshop (2006)Google Scholar
  20. 20.
    Kumar, N., Berg, A., Belhumeur, P.: Describable visual attributes for face verification and image search. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1962–1977 (2011)CrossRefGoogle Scholar
  21. 21.
    Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  22. 22.
    Layne, R., Hospedales, T.M., Gong, S.: Person re-identification by attributes. In: British Machine Vision Conference (2012)Google Scholar
  23. 23.
    Layne, R., Hospedales, T.M., Gong, S.: Towards person identification and re-identification with attributes. In: European Conference on Computer Vision, First International Workshop on Re-identification, Florence (2012)Google Scholar
  24. 24.
    Liu, C., Gong, S., Loy, C.C., Lin, X.: Person re-identification: what features are important? In: European Conference on Computer Vision, First International Workshop on Re-identification, Florence (2012)Google Scholar
  25. 25.
    Liu, J., Kuipers, B.: Recognizing human actions by attributes. In: IEEE Conference on Computer Vision and Pattern Recognition pp. 3337–3344 (2011)Google Scholar
  26. 26.
    Liu, D., Nocedal, J.: On the limited memory method for large scale optimization. Math. Program. B 45(3), 503–528 (1989)Google Scholar
  27. 27.
    Loy, C.C., Xiang, T., Gong, S.: Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding. Int. J. Comput. Vision 90(1), 106–129 (2010)CrossRefGoogle Scholar
  28. 28.
    Mackay, D.J.C.: Information Theory, Inference, and Learning Algorithms, 4th edn. Cambridge University Press, Cambridge (2003)Google Scholar
  29. 29.
    Madden, C., Cheng, E.D., Piccardi, M.: Tracking people across disjoint camera views by an illumination-tolerant appearance representation. Mach. Vis. Appl. 18(3–4), 233–247 (2007)CrossRefMATHGoogle Scholar
  30. 30.
    Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge, MA, (2012)Google Scholar
  31. 31.
    Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing for Computer Vision, 3rd edn. Academic Press, Waltham (2012)Google Scholar
  32. 32.
    Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer-Verlag, Newyork (2006)Google Scholar
  33. 33.
    Nortcliffe, T.: People Analysis CCTV Investigator Handbook. Home Office Centre of Applied Science and Technology, UK Home Office (2011)Google Scholar
  34. 34.
    Orabona, F., Jie, L.: Ultra-fast optimization algorithm for sparse multi kernel learning. In: International Conference on Machine Learning (2011)Google Scholar
  35. 35.
    Orabona, F.: DOGMA: a MATLAB toolbox for online learning (2009)Google Scholar
  36. 36.
    Platt, J.C.: Probabilities for SV machines. In: Advances in Large Margin Classifiers. MIT Press, Cambridge (1999)Google Scholar
  37. 37.
    Prosser, B., Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: British Machine Vision Conference (2010)Google Scholar
  38. 38.
    Satta, R., Fumera, G., Roli, F.: A general method for appearance-based people search based on textual queries. In: European Conference on Computer Vision, First International Workshop on Re-Identification (2012)Google Scholar
  39. 39.
    Schneiderman, R.: Trends in video surveillance give dsp an apps boost. IEEE Signal Process. Mag. 6(27), 6–12 (2010)Google Scholar
  40. 40.
    Schölkopf, B., Smola, A.J.: Learning with kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA (2002)Google Scholar
  41. 41.
    Siddiquie, B., Feris, R.S., Davis, L.S.: Image ranking and retrieval based on multi-attribute queries. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)Google Scholar
  42. 42.
    Smyth, P.: Bounds on the mean classification error rate of multiple experts. Pattern Recogn. Lett. 17, 1253–1257 (1996)Google Scholar
  43. 43.
    Vaquero, D.A., Feris, R.S., Tran, D., Brown, L., Hampapur, A., Turk, M.: Attribute-based people search in surveillance environments. In: IEEE International Workshop on the Applications of Computer Vision, Snowbird, Utah (2009)Google Scholar
  44. 44.
    Walt, C.V.D., Barnard, E.: Data characteristics that determine classifier performance. In: Annual Symposium of the Pattern Recognition Association of South Africa (2006)Google Scholar
  45. 45.
    Williams, D.: Effective CCTV and the challenge of constructing legitimate suspicion using remote visual images. J. Invest. Psychol. Offender Profil. 4(2), 97–107 (2007)Google Scholar
  46. 46.
    Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference (2009)Google Scholar
  47. 47.
    Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)Google Scholar
  48. 48.
    Zheng, W.S., Gong, S., Xiang, T.: Transfer re-identification : from person to set-based verification. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  49. 49.
    Zheng, W.S., Gong, S., Xiang, T.: Quantifying and Transferring Contextual Information in Object Detection. IEEE Trans. Pattern Anal. Mach. Intell. 1(8), 762–777 (2011)Google Scholar
  50. 50.
    Zheng, W.S., Gong, S., Xiang, T.: Re-identification by Relative Distance Comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRefGoogle Scholar
  51. 51.
    Zhu, X., Wu, X.: Class Noise vs. Attribute Noise: A Quantitative Study of Their Impacts. Artif. Intell. Rev. 22(1), 177–210 (2004)Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Ryan Layne
    • 1
  • Timothy M. Hospedales
    • 1
  • Shaogang Gong
    • 1
  1. 1.Queen Mary University of LondonLondonUK

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