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Adaptive Alignment Network for Person Re-identification

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11296))

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Abstract

Person re-identification aims at identifying a target pedestrian across non-overlapping camera views. Pedestrian misalignment, which mainly arises from inaccurate person detection and pose variations, is a critical challenge for person re-identification. To address this, this paper proposes a new Adaptive Alignment Network (AAN), towards robust and accurate person re-identification. AAN automatically aligns pedestrian images from coarse to fine by learning both patch-wise and pixel-wise alignments, leading to effective pedestrian representation invariant to the variance of human pose and location across images. In particular, AAN consists of a patch alignment module, a pixel alignment module and a base network. The patch alignment module estimates the alignment offset for each image patch and performs patch-wise alignment with the offsets. The pixel alignment module is for fine-grained pixel-wise alignment. It learns the subtle local offset for each pixel and produces finely aligned feature map. Extensive experiments on three benchmarks, i.e., Market1501, DukeMTMC-reID and MSMT17 datasets, have demonstrated the effectiveness of the proposed approach.

X. Zhu and J. Liu—Equal contribution.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 61622211, 61472392, and 61620106009 as well as the Fundamental Research Funds for the Central Universities under Grant WK2100100030.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Xierong Zhu, Jiawei Liu, Hongtao Xie & Zheng-Jun Zha

Authors
  1. Xierong Zhu
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  2. Jiawei Liu
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  3. Hongtao Xie
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  4. Zheng-Jun Zha
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Corresponding author

Correspondence to Zheng-Jun Zha .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

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Zhu, X., Liu, J., Xie, H., Zha, ZJ. (2019). Adaptive Alignment Network for Person Re-identification. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-05716-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05715-2

  • Online ISBN: 978-3-030-05716-9

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