[1]ZHANG Zhi,BI Xiaojun.Clustering approach based on style transfer for unsupervised person re-identification[J].CAAI Transactions on Intelligent Systems,2021,16(1):48-56.[doi:10.11992/tis.202012014]
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Clustering approach based on style transfer for unsupervised person re-identification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 48-56 Column: 学术论文—机器感知与模式识别 Public date: 2021-01-05
Title:
Clustering approach based on style transfer for unsupervised person re-identification
Author(s):
ZHANG Zhi1;  BI Xiaojun2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. School of Information Engineering, Minzu University of China, Beijing 100081, China
Keywords:
machine vision; pedestrian re-identification; unsupervised; clustering; style transformation; generative adversarial networks; residual block; cross domain
CLC:
TP391
DOI:
10.11992/tis.202012014
Abstract:
The substantial difference between the source and target domains is the most crucial factor affecting the performance of unsupervised person re-identification models. The clustering-based unsupervised person re-identification method alleviates the problem to a certain extent by mining the similarity between the target domain, but it does not fundamentally eliminate the discrepancy between the domains. This paper proposes a clustering approach based on cross-domain style transfer for unsupervised pedestrian re-identification. First, to avoid the difference between domains in clustering-based unsupervised person re-identification models, the across-domain style transfer method based on a generative adversarial network is introduced into the clustering process. It transfers the source domain data to the target domain style data, which directly reduces the difference between domains and improves the recognition effect of the model. Second, the generator of cross-domain style transfer model has a single transfer scale and low efficiency of characteristics information transfer. A new type of residual block is proposed to replace the original residual block; then, it is inserted into the generator to achieve up-sampling and down-sampling. The specific generator has more characteristics of the scale transfer, and it transmits information more effectively. The cross-domain style transfer model can better transfer the style of the source and target domains, further reduce the difference between the two domains, and improve the recognition effect of the overall model. Extensive experiments were implemented on Market1501 and Duke-MTMC-Reid datasets to examine the proposed method, and the results showed that the proposed improved method achieved a better recognition effect.
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