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Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification

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Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

Abstract

A comparative analysis of various visual descriptors is presented in this chapter. The descriptors utilize many aspects of image data: colour, texture, gradient, and statistical moments. The descriptor list is supplemented with local features calculated in close vicinity of key points found automatically in the image. The goal of the analysis is to find descriptors that are best suited for particular task, i.e. re-identification of objects in a multi-camera environment. The analysis is performed using two datasets containing images of humans and vehicles recorded with different cameras. For the purpose of descriptor evaluation, scatter and clustering measures are supplemented with a new measure that is derived from calculating direct dissimilarities between pairs of images. In order to draw conclusions from multi-dataset analysis, four aggregation measures are introduced. They are meant to find descriptors that provide the best identification effectiveness, based on the relative ranking, and simultaneously are characterized with large stability (invariance to the selection of objects in the dataset). Proposed descriptors are evaluated practically with object re-identification experiments involving four classifiers to detect the same object after its transition between cameras’ fields of view. The achieved results are discussed in detail and illustrated with figures.

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Acknowledgments

Research is subsidized by the European Commission within FP7 project “ADDPRIV” (“Automatic Data relevancy Discrimination for a PRIVacy-sensitive video surveillance” , Grant Agreement No. 261653). The authors wish to thank the Gdańsk Science and Technology Park for their help in establishing the test bed for the experiments described in the chapter.

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Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland

    Piotr Dalka, Damian Ellwart, Grzegorz Szwoch, Karol Lisowski, Piotr Szczuko & Andrzej Czyżewski

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  1. Piotr Dalka
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  2. Damian Ellwart
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  3. Grzegorz Szwoch
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  4. Karol Lisowski
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  5. Piotr Szczuko
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  6. Andrzej Czyżewski
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Correspondence to Piotr Dalka .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Dalka, P., Ellwart, D., Szwoch, G., Lisowski, K., Szczuko, P., Czyżewski, A. (2015). Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_12

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_12

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