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Vehicle Re-identification in Context

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Pattern Recognition (GCPR 2018)

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

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Abstract

Existing vehicle re-identification (re-id) evaluation benchmarks consider strongly artificial test scenarios by assuming the availability of high quality images and fine-grained appearance at an almost constant image scale, reminiscent to images required for Automatic Number Plate Recognition, e.g. VeRi-776. Such assumptions are often invalid in realistic vehicle re-id scenarios where arbitrarily changing image resolutions (scales) are the norm. This makes the existing vehicle re-id benchmarks limited for testing the true performance of a re-id method. In this work, we introduce a more realistic and challenging vehicle re-id benchmark, called Vehicle Re-Identification in Context (VRIC). In contrast to existing vehicle re-id datasets, VRIC is uniquely characterised by vehicle images subject to more realistic and unconstrained variations in resolution (scale), motion blur, illumination, occlusion, and viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60 different cameras at heterogeneous road traffic scenes in both day-time and night-time. Given the nature of this new benchmark, we further investigate a multi-scale matching approach to vehicle re-id by learning more discriminative feature representations from multi-resolution images. Extensive evaluations show that the proposed multi-scale method outperforms the state-of-the-art vehicle re-id methods on three benchmark datasets: VehicleID, VeRi-776, and VRIC (Available at http://qmul-vric.github.io).

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Author information

Authors and Affiliations

  1. Queen Mary University of London, London, E1 4NS, UK

    Aytaç Kanacı & Shaogang Gong

  2. Vision Semantics Limited, London, E1 4NS, UK

    Xiatian Zhu

Authors
  1. Aytaç Kanacı
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  2. Xiatian Zhu
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  3. Shaogang Gong
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Corresponding author

Correspondence to Aytaç Kanacı .

Editor information

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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Kanacı, A., Zhu, X., Gong, S. (2019). Vehicle Re-identification in Context. 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_26

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

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