IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i11p289-d681676.html
   My bibliography  Save this article

Person Re-Identification by Low-Dimensional Features and Metric Learning

Author

Listed:
  • Xingyuan Chen

    (School of Computer Science and Engineering, Shanghai University, Shanghai 200444, China)

  • Huahu Xu

    (School of Computer Science and Engineering, Shanghai University, Shanghai 200444, China
    Shanghai Shangda Hairun Information System Co., Ltd., Shanggai 200072, China)

  • Yang Li

    (School of Computer Science and Engineering, Shanghai University, Shanghai 200444, China)

  • Minjie Bian

    (School of Computer Science and Engineering, Shanghai University, Shanghai 200444, China
    Shanghai Shangda Hairun Information System Co., Ltd., Shanggai 200072, China)

Abstract

Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There are also few studies that combine the powerful learning capabilities of deep learning with color features. Therefore, we hope to use the advantages of both to design a model with low computational resource consumption and excellent performance to solve the task of person re-identification. In this paper, we designed a color feature containing relative spatial information, namely the color feature with spatial information. Then, bidirectional long short-term memory (BLSTM) networks with an attention mechanism are used to obtain the contextual relationship contained in the hand-crafted color features. Finally, experiments demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable.

Suggested Citation

  • Xingyuan Chen & Huahu Xu & Yang Li & Minjie Bian, 2021. "Person Re-Identification by Low-Dimensional Features and Metric Learning," Future Internet, MDPI, vol. 13(11), pages 1-12, November.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:11:p:289-:d:681676
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/11/289/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/11/289/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:11:p:289-:d:681676. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.