Person re-identification (ReID) is mainly aimed at establishing correct identity correspondence among moving person collected by multiple cameras. Extending labeled data sets with pseudo-labels is one of the common methods of ReID. However, single evaluation standards and fixed screening pseudo-label methods make pseudo-labels gradually weaken their update rate. Based on that, we propose a semi-supervised adaptive stepwise learning (SSAS) method for accelerating the update of pseudo-labels. Using the concept of Kullback–Leibler divergence, a more global pseudo-label update idea (GPLU) is proposed, an evaluation criterion of pseudo-labels is designed to satisfy two conditions: The first is to use simple tracklets as pseudo-label data in the early stage, and the second is to gradually add complex and diverse tracklets as pseudo-label data in the iterative process. Our proposed adaptive pseudo-label screening strategy steadily improves the recognition accuracy of ReID. In addition, we conduct extensive experiments on canonical data sets and the evaluation results suggest the superiority of our method.

行人重识别（ReID）主要是为了在多个摄像头采集到的移动人之间建立正确的身份对应关系。使用伪标签扩展标记数据集是 ReID 的常用方法之一。然而，单一的评价标准和固定的筛选伪标签方法使得伪标签的更新率逐渐减弱。在此基础上，我们提出了一种半监督自适应逐步学习（SSAS）方法来加速伪标签的更新。利用Kullback–Leibler散度的概念，提出了一种更全局的伪标签更新思想（GPLU），设计了一个伪标签的评价标准，满足两个条件：第一个是使用简单的轨迹作为伪标签数据早期阶段，二是在迭代过程中逐步添加复杂多样的tracklets作为伪标签数据。我们提出的自适应伪标签筛选策略稳步提高了 ReID 的识别精度。此外，我们对规范数据集进行了大量实验，评估结果表明我们方法的优越性。