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Study of cross-domain person re-identification based on DCGAN

  • 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data
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Abstract

Person re-identification(re-ID) techniques have been rapidly improving with the development of deep neural networks, and the accuracy of fully supervised re-ID models is already very high. However, when person re-identification models with supervised learning are used directly in unlabeled target scenes, the accuracy is greatly reduced. This is due to the large disparity between the domains of different datasets, such as resolution, lighting changes and occlusion. At the same time, the existing re-ID methods based on domain transfer have the problem of blurred images due to big gap between domains. Therefore, in this paper, a DCGAN (Deep Convolutional Generative Adversarial Network) is added to the unsupervised cross-domain re-ID model, which can allow the target domain distribution to be adequately fitted to the labeled domain distribution, so that the re-ID model trained in source domain can be used on the target domain without large fluctuations due to domain shift. By comparing with the UMDL(Unsupervised Multi-task Dictionary Learning) method, rank-1 and rank-5 are improved by 9.61% and 17.81%, respectively, when the model trained from Market-1501 is used in the GRID dataset, and by 9.52% and 14.14%, respectively, when compared with the GAN(Generative Adversarial Networks)-based method under the same experiment.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No.42075007), the Open Grants of the State Key Laboratory of Severe Weather(No.2021LASW-B19), the Open Project of Provincial Key Laboratory for Computer Information Processing Technology under Grant KJS1935, Soochow University, and the Priority Academic Program Development of Jiangsu Higher Education Institutions, Graduate Scientific Research Innovation Program of Jiangsu Province under Grant no. KYCX21_1015.

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Authors and Affiliations

  1. School of Computer, Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science, Technology, Nanjing, China

    Wei Fang, Weinan Yi & Lin Pang

  2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

    Wei Fang

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China

    Wei Fang

  4. Texas Tech University, Lubbock, USA

    Victor S. Sheng

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  1. Wei Fang
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Correspondence to Weinan Yi.

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Fang, W., Yi, W., Pang, L. et al. Study of cross-domain person re-identification based on DCGAN. Multimed Tools Appl 81, 36551–36565 (2022). https://doi.org/10.1007/s11042-022-13526-3

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  • DOI: https://doi.org/10.1007/s11042-022-13526-3

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