为了降低监督学习需要大量高质量带标签数据的影响，提出了一种基于正则化约束的半监督行人重识别方法。该方法构造出相同初始化参数的双模型网络，即教师网络和学生网络。在训练过程中随机使用部分有标签数据和大量无标签数据作为双模型的输入，同时利用正则化对双模型网络的参数进行约束，使其对于同一个有标签或无标签输入数据的输出结果保持一致。在此基础上，教师网络利用随机梯度下降法优化得到其所需参数，学生网络则利用教师网络优化过的参数进行权重加权移动平均迭代得到其所需参数。实验结果表明，该方法在利用部分有标签和大量无标签组成的market1501数据集上的Rank-1和mAP比原ABD-Net方法的Rank-1和mAP均有提高，证明了该算法在少量有标签下的学习具有有效性。

To reduce the impact of supervised learning requiring a large amount of high -quality labeled data, a semi-supervised pedestrian re -identification method based on regularization constraint is proposed. This method constructs a dual -model network with the same initialization parameters, namely the teacher network and the student network. During the training process, some labeled data and a large amount of unlabeled data are randomly used as the input of the dual model, and the parameters of the dual model network are constrained by regularization, so that the output results of the same labeled or unlabeled input data remain consistent. On this basis, the teacher network uses the stochastic gradient descent method to optimize the required parameters, and the student network uses the parameters optimized by the teacher network to perform the weighted moving average iteration to obtain the required parameters. The experimental results show that the Rank-1 and mAP of the proposed method on the market1501 dataset with partial labels and a large number of unlabeled data sets are improved compared with the Rank-1 and mAP of the original ABD-Net method, which proves that this algorithm is effective by learning with a small number of labels.