跨摄像头场景中依赖面向标签映射关系的学习以提高识别精度，有监督行人重识别模型虽然识别精度较好，但存在可扩展问题，诸如算法识别精度严重依赖有效的监督信息，算法实时性差等；针对上述问题，提出一种基于软多标签的无监督行人重识别算法。为了提高标签匹配精度，首先利用软多标签逼近真实标签，通过计算参考数据集和参考代理在软多标签函数中的损失函数，预训练参考数据集，并构建预训练与训练结果的映射模型。再通过生成数据和真实数据分布的最小距离的期望即简化的2-Wasserstein距离计算相机视图中软多标签均值和标准差得到损失函数，解决跨视域标签一致性问题。为了提高软多标签对未标记目标数据集的有效性，计算联合嵌入损失，挖掘不同类别间的相似对，纠正跨域分布错位。针对残差网络训练时长和无监督学习精度低的问题，通过结合压缩激励网络(SENet)和多层级深度特征融合改进残差网络的结构，提高训练速度和精度。实验结果表明，该方法在标准数据集下的首位命中率和平均精度均值优于先进相关算法。

n cross-camera scenarios, it relies on the learning of label mapping relationships to improve recognition accuracy. The supervised person re-identification model has better recognition accuracy, but there are scalability problems. For example, the accuracy of algorithm identification relies heavily on effective supervised information. When adding a small amount of data in the classification process, all data needs to be reprocessed, resulting in poor real-time performance. Aiming at the above problems, an unsupervised person re-identification algorithm based on soft label is proposed. In order to improve the accuracy of label matching, first, learn soft multilabel to make it close to the real label, and obtain the reference agent by calculating the loss function of the reference data set to achieve the purpose of pre-training the reference data set. Then, calculate the expected value of the minimum distance between the generated data and the real data distribution (using the simplified 2-Wasserstein distance), calculate the mean and standard deviation vector of the soft multilabel in the camera view, and the resulting loss function can solve cross-view domain label consistency issues. In order to improve the validity of the soft tag on the unmarked target data set, the joint embedding loss is calculated, the similar pairs between different categories are mined, and the cross-domain distribution misalignment is corrected. In view of the problem that the residual network training duration and the unsupervised learning accuracy are low, the structure of the residual network is improved by combining the SENet and fusing multi-level depth feature to improve the training speed and accuracy. The experimental results show that the rank-1 and mAP are better than advanced correlation algorithms.