基于异构卷积神经网络集成的无监督行人重识别方法OACSCDCSTPCD

Person re-identification(re-ID)aims to identify a person's images across different cameras.However,the domain bias between different datasets makes it a challenge for re-ID models trained on one dataset to be adapted to anoth-er.A variety of unsupervised domain adaptation methods tend to transfer learned knowledge from one domain to another by optimizing with pseudo-labels.However,these methods introduce a large number of noisy labels through one-shot clus-tering,which hinders the retraining process and limits generalization.To mitigate the impact of noisy pseudo-labels,this pa-per proposes an unsupervised person re-identification method based on an ensemble of heterogeneous convolutional neural networks.The framework does not apply any manual labeling information,automatically infers the relationship between pe-destrian images in the target domain,and a cooperative trusted instance selection mechanism is established to select pseudo-labels with high credibility.By constructing a dual-branch heterogeneous network,a variety of different pedestrian features are learned,and memory structures are designed to store the life-long features during the training stage,which could reduce the fluctuation of noise labels,and improve the robustness of the model.Comprehensive experimental results have demon-strated that our proposed method can achieve excellent performances on benchmark datasets.And mAP is increased to 85.4％and 74.8％on Market1501 and DukeMTMC-reID,respectively.