基于跨域特征关联与聚类的无监督行人重识别

行人重识别的目标是利用计算机视觉技术在多个摄像头采集的图像序列或视频中识别目标行人，基于监督学习的行人重识别算法虽然提高了目标的识别性能，但难以解决行人重识别中无标注目标域的域内变化问题，从而导致无标注数据检索准确度低。提出一种基于域自适应的无监督行人重识别算法，其主要由ResNet-50骨干网络、跨域特征提取器和用以存储目标域特征的特征库组成。通过跨域特征提取器融合行人样本在特征图与通道方向的特征，以挖掘不同行人重识别数据集间潜在的特征关联关系，同时为无标注目标数据集样本内的特征关联构建特征库，在无任何标注信息的情况下从一个未知数据集学习判别性特征。实验结果表明，该算法在源域DukeMTMC-reID/Market-1501和目标域Market-1501/DukeMTMC-reID的首位命中率相较于ECN算法分别提高8.9和6.8个百分点，能够提高模型在未知数据集上的泛化能力和无监督跨域行人重识别的准确度。

Unsupervised Pedestrian Re-Identification Based on Cross-Domain Feature Association and Clustering

The objective of pedestrian re-identification is to use computer vision technology to identify the target pedestrian in image sequence or video collected by multiple cameras.Although the pedestrian re-identification algorithm based on supervised learning improves the target re-identification performance,it is difficult to solve the problem of intra domain variation of unlabeled attention domain in pedestrian re-identification,resulting in low retrieval accuracy of unlabeled data.To solve this problem,this paper proposes an unsupervised pedestrian re-identification algorithm based on a domain adaptive method,which is mainly composed of ResNet-50 backbone network,cross-domain feature extractor and feature database to store the characteristics of the target domain.The cross-domain feature extractor is used to fuse the features of pedestrian samples in the feature map and channel direction,to mine the potential feature association between different pedestrian re-identification datasets.A feature database is constructed for the feature association in the unlabeled target dataset,and the discriminant features are learned from an unknown dataset without any labeling information.The experimental results show that the Rank-1 of the algorithm is 8.9 and 6.8 percentage points higher than that of the ECN algorithm in the source domain DukeMTMC-reID/Market-1501 and target domain Market-1501/DukeMTMC-reID,respectively,effectively improving the generalization ability of the model on unknown datasets and the accuracy of unsupervised cross domain pedestrian re-identification.