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Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12347))

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Abstract

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches.

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Acknowledgment

This research was supported in part by the Ministry of Science and Technology of Taiwan (MOST 108-2633-E-002-001), National Taiwan University (NTU-108L104039), Intel Corporation, Delta Electronics and Compal Electronics.

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Authors and Affiliations

  1. Graduate Institute of Electronic Engineering, National Taiwan University, Taipei, Taiwan

    Tsai-Shien Chen, Chih-Ting Liu, Chih-Wei Wu & Shao-Yi Chien

  2. NTU IoX Center, National Taiwan University, Taipei, Taiwan

    Tsai-Shien Chen, Chih-Ting Liu, Chih-Wei Wu & Shao-Yi Chien

Authors
  1. Tsai-Shien Chen
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  2. Chih-Ting Liu
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  3. Chih-Wei Wu
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  4. Shao-Yi Chien
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Corresponding author

Correspondence to Shao-Yi Chien .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, TS., Liu, CT., Wu, CW., Chien, SY. (2020). Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-58536-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58535-8

  • Online ISBN: 978-3-030-58536-5

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