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View-Aware Person Re-identification

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Book cover Pattern Recognition (GCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

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Abstract

Appearance-based person re-identification (PRID) is currently an active and challenging research topic. Recently proposed approaches have mostly dealt with low- and middle-level processing of images. Furthermore, there is very limited research that has focused on view information. View variation limits the performance of most approaches because a person’s appearance from one view can be completely different from that of another view, which makes the re-identification challenging. In this work, we study the influence of the view on PRID and propose several fusion strategies that utilize multi-view information to handle the PRID problem. We perform experiments on a re-mapped version of Market-1501 dataset and an internal dataset. Our proposed multi-view strategy increases the recognition rate at rank-one by a large margin in comparison with that obtained via random view matching or multi-shot.

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Notes

  1. 1.

    Including conference papers, journals, arxiv and technical reports.

  2. 2.

    Cameras 1–3 from the original dataset were used for virtual camera 1, and cameras 4–6 were used for virtual camera 2.

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Authors and Affiliations

  1. Computer Vision Research Lab, Robert Bosch GmbH, Hildesheim, Germany

    Gregor Blott & Jie Yu

  2. Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hannover, Germany

    Gregor Blott & Christian Heipke

Authors
  1. Gregor Blott
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  2. Jie Yu
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  3. Christian Heipke
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Corresponding author

Correspondence to Gregor Blott .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Blott, G., Yu, J., Heipke, C. (2019). View-Aware Person Re-identification. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_4

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  • Online ISBN: 978-3-030-12939-2

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