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Attributes-Based Re-identification

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Person Re-Identification

Part of the book series: Advances in Computer Vision and Pattern Recognition ((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.

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Notes

  1. 1.

    We provide our annotations here: http://www.eecs.qmul.ac.uk/~rlayne/

  2. 2.

    Our experiments on LIBSVM performance versus attribute training time show the intersection kernel as being a good combination of calculation time and accuracy. For example, training the attribute ontology results in 65.4 % mean accuracy with 0.8 h training for the intersection kernel, as compared to the \(\chi ^2\) kernel (63.8 % with 4.1 h), the RBF kernel (65.9 % with 0.76 h and the linear kernel (61.8 % with 1.2 h) respectively with LIBSVM. Although RBF is computed slightly faster and has similar accuracy, we select the intersection kernel overall, since the RBF kernel would require cross-validating over a second parameter. Providing LIBSVM with pre-built kernels reduces training time considerably in all cases.

References

  1. Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: European Conference on Machine Learning (2004)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. Cheng, D., Cristani, M., Stoppa, M., Bazzani, L.: Custom pictorial structures for re-identification. In: British Machine Vision Conference (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  10. Ferrari, V., Zisserman, A.: Learning visual attributes. In: Neural Information Processing Systems (2007)

    Google Scholar 

  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. 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. 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. He, H., Garcia, E.A.: Learning from imbalanced data. In: IEEE Transactions on Data and Knowledge Engineering, vol. 21 (2009)

    Google Scholar 

  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. 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. 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. 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. Keval, H.: CCTV Control room collaboration and communication: does it Work? In: Human Centred Technology Workshop (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Layne, R., Hospedales, T.M., Gong, S.: Person re-identification by attributes. In: British Machine Vision Conference (2012)

    Google Scholar 

  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. 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. 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. Liu, D., Nocedal, J.: On the limited memory method for large scale optimization. Math. Program. B 45(3), 503–528 (1989)

    Google Scholar 

  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)

    Article  Google Scholar 

  28. Mackay, D.J.C.: Information Theory, Inference, and Learning Algorithms, 4th edn. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  30. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge, MA, (2012)

    Google Scholar 

  31. Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing for Computer Vision, 3rd edn. Academic Press, Waltham (2012)

    Google Scholar 

  32. Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer-Verlag, Newyork (2006)

    Google Scholar 

  33. Nortcliffe, T.: People Analysis CCTV Investigator Handbook. Home Office Centre of Applied Science and Technology, UK Home Office (2011)

    Google Scholar 

  34. Orabona, F., Jie, L.: Ultra-fast optimization algorithm for sparse multi kernel learning. In: International Conference on Machine Learning (2011)

    Google Scholar 

  35. Orabona, F.: DOGMA: a MATLAB toolbox for online learning (2009)

    Google Scholar 

  36. Platt, J.C.: Probabilities for SV machines. In: Advances in Large Margin Classifiers. MIT Press, Cambridge (1999)

    Google Scholar 

  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. 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. Schneiderman, R.: Trends in video surveillance give dsp an apps boost. IEEE Signal Process. Mag. 6(27), 6–12 (2010)

    Google Scholar 

  40. Schölkopf, B., Smola, A.J.: Learning with kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA (2002)

    Google Scholar 

  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. Smyth, P.: Bounds on the mean classification error rate of multiple experts. Pattern Recogn. Lett. 17, 1253–1257 (1996)

    Google Scholar 

  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. 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. 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. Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference (2009)

    Google Scholar 

  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. 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. 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. Zheng, W.S., Gong, S., Xiang, T.: Re-identification by Relative Distance Comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)

    Article  Google Scholar 

  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 

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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.

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Authors and Affiliations

  1. Queen Mary University of London, London, UK

    Ryan Layne, Timothy M. Hospedales & Shaogang Gong

Authors
  1. Ryan Layne
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  2. Timothy M. Hospedales
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  3. Shaogang Gong
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Corresponding author

Correspondence to Ryan Layne .

Editor information

Editors and Affiliations

  1. Queen Mary University, London, United Kingdom

    Shaogang Gong

  2. University of Verona, Italy

    Marco Cristani

  3. National University of Singapore, Singapore

    Shuicheng Yan

  4. The Chinese University of Hong Kong, Shatin, Hong Kong SAR

    Chen Change Loy

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Layne, R., Hospedales, T.M., Gong, S. (2014). Attributes-Based Re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_5

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_5

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  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

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