Domain Adapted Person Re-identification Algorithm Based on Joint Network

doi:10.3969/j.issn.1006-2475.2023.06.009

Abstract

Abstract: Aiming at the problems of insufficient use of image details in the domain adaptation method and low accuracy of cross-domain person re-recognition due to data distribution differences between domains, a domain-adapted person re-recognition algorithm based on joint network was designed. Firstly, in order to improve the model's ability to obtain details, an attention mechanism module is embedded in the network, and an asymmetric multi-granularity network was designed to supplement the missing details in the global feature. Secondly, in order to alleviate the influence caused by inter-domain differences, online correlation consistency loss is introduced on the basis of CycleGAN network, and sample generator is trained to generate source domain samples with target domain style to reduce inter-domain data distribution differences. Then, in the domain adaptation stage, the method based on clustering joint network is designed, and the classification probability output by student model in the joint network is used as the supervision information to supervise the training of teacher network, so as to avoid the error amplification caused by clustering hard pseudo-label in training. Finally, Momentum Contrast Loss （MoCo Loss） is introduced to alleviate the influence of noise pseudo-labels to improve the adaptive ability of the model. Experiments are carried out on Market-1501 and DukeMTMC-Reid, and the results show that the algorithm has certain competitiveness compared with the mainstream algorithms.

Key words: person re-identification, domain adaptation, multigrain features, attention mechanism, pseudo tags, domain style migration

CLC Number:

TP391

LI Guo-xin, QU Han-bing, ZHU Cheng-bo, WANG Xin-xuan, HU Jia-bao. Domain Adapted Person Re-identification Algorithm Based on Joint Network[J]. Computer and Modernization, 2023, 0(06): 48-55.

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