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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.

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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

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  1. Ryan Layne
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  2. Timothy M. Hospedales
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  3. Shaogang Gong
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Correspondence to Ryan Layne .

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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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