Cross-domain person re-identification using heterogeneous convolutional network

Zhang, Zhong and Wang, Yanan and Liu, Shuang and Xiao, Baihua and Durrani, Tariq S. (2022) Cross-domain person re-identification using heterogeneous convolutional network. IEEE Transactions on Circuits and Systems for Video Technology, 32 (3). pp. 1160-1171. ISSN 1051-8215 (https://doi.org/10.1109/TCSVT.2021.3074745)

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Abstract

Person re-identification (Re-ID) is a challenging task due to variations in pedestrian images, especially in cross-domain scenarios. The existing cross-domain person Re-ID approaches extract the feature from single pedestrian image, but they ignore the correlations among pedestrian images. In this paper, we propose Heterogeneous Convolutional Network (HCN) for cross-domain person Re-ID, which learns the appearance information of pedestrian images and the correlations among pedestrian images simultaneously. To this end, we first utilize Convolutional Neural Network (CNN) to extract the appearance features for pedestrian images. Then we construct a graph in the target dataset where the appearance features are treated as the nodes and the similarity represents the linkage between the nodes. Afterwards, we propose Dual Graph Convolution (DGConv) to explicitly learn the correlation information from the similar and dissimilar samples, which could avoid the over-smoothing caused by the fully connected graph. Furthermore, we design HCN as a multi-branch structure to mine the structural information of pedestrians. We conduct extensive evaluations for HCN on three datasets, i.e. Market-1501, DukeMTMC-reID and MSMT17, and the results demonstrate that HCN is superior to the state-of-the-art methods.

  • Item type:Article
    ID code:76921
    Dates:
    Date
    Event
    March 2022
    Published
    21 April 2021
    Published Online
    17 April 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:30 Jun 2021 14:47
    Last modified:20 Nov 2024 01:21
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/76921
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