电子学报2023，Vol.51Issue(10)：2902-2914,13.DOI:10.12263/DZXB.20220467
基于异构卷积神经网络集成的无监督行人重识别方法
An Unsupervised Person Re-Identification Method Based on Heterogeneous Convolutional Neural Networks Ensemble
摘要
Abstract
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.关键词
行人重识别/异构卷积神经网络/协作可信实例选择/噪声平滑/自适应更新Key words
person re-identification/heterogeneous convolutional neural networks/collaborative trusted instance se-lection/noise smoothing/adaptive updating分类
信息技术与安全科学引用本文复制引用
彭锦佳,王辉兵..基于异构卷积神经网络集成的无监督行人重识别方法[J].电子学报,2023,51(10):2902-2914,13.基金项目
国家自然科学基金(No.62002041) （No.62002041）
河北大学高层次人才科研启动项目(No.521100221029)National Natural Science Foundation of China(No.62002041) （No.521100221029）
Hebei University High-level Scientific Research Foundation for the Introduction of Talent(No.521100221029) （No.521100221029）
