PGAN: part-based nondirect coupling embedded GAN for person re-identification

Zhang, Y., Jin, Y., Chen, J., Kan, S., Cen, Y. and Cao, Q. (2020) PGAN: part-based nondirect coupling embedded GAN for person re-identification. IEEE MultiMedia, 27(3), pp. 23-33. (doi: 10.1109/mmul.2020.2999445)

217561.pdf - Accepted Version



The block-based representation learning method has been proven to be a very effective method for person re-identification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, Part-based Nondirect Coupling (PNC) representation learning method is proposed by introducing a similarity measure loss to constrain features of different blocks. Moreover, Part-based Nondirect Coupling Embedded GAN (PGAN) method is proposed, which aims to extract more common features of different postures of a same person. In this way, the extracted features of the network are robust for posture changes of a person, and there are no auxiliary pose information and additional computational cost required in the test stage. Experimental results on public datasets show that our proposed method achieves good performances, especially, it outperforms the state-of-the-art GAN-based methods for person Re-ID.

Item Type:Articles
Additional Information:This work was supported in part by the National Key R&D Program of China 2019YFB2204200, in part by the Central Universities under Grant 2019YJS039, in part by the National Natural Science Foundation of China under Grant 61872034 and 61972030, in part by the Beijing Municipal Natural Science Foundation under Grant 4202055, in part by the Natural Science Foundation of Guizhou Province under Grant [2019]1064, in part by the Science and Technology Program of Guangzhou under grant 201804010271.
Glasgow Author(s) Enlighten ID:Cao, Dr Qi
Authors: Zhang, Y., Jin, Y., Chen, J., Kan, S., Cen, Y., and Cao, Q.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE MultiMedia
ISSN (Online):1941-0166
Published Online:02 June 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE MultiMedia 27(3): 23-33
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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