Person re-identification is a challenging research issue in computer vision and has a broad application prospect in intelligent security. In recent years, with the emergence of large-scale person datasets and the rapid development of deep learning, many outstanding results have been achieved in person re-identification researches, which mainly involves two critical technologies: feature extraction and distance metric. Among them, feature extraction has been well summarized in the current literature of person re-identification, but there is no systematic analysis of the distance metric method in the current review literature. However, effective and reliable distance metric is crucial to improve the accuracy of person re-identification. Therefore, it is necessary to systematically review and summarize the metric learning methods in person re-identification, so as to provide some references for the researchers of metric learning. In this paper, we make a comprehensive analysis of metric learning methods in the past five years, which can be summarized into three aspects: distance metric method, metric learning algorithm, and re-ranking for the metric results. Then, we compare the performance of some representative metric learning methods and discuss them in-depth. Finally, we make a prospect for the future research direction of metric learning in person re-identification.

人员重新识别是计算机视觉中一个具有挑战性的研究问题，在智能安全中具有广阔的应用前景。近年来，随着大规模人员数据集的出现和深度学习的迅猛发展，在人员重新识别研究中取得了许多杰出的成果，主要涉及两项关键技术：特征提取和距离度量。其中，特征提取在现有的人员重新识别文献中得到了很好的总结，但是在目前的文献中还没有对距离度量方法进行系统的分析。但是，有效而可靠的距离度量对于提高人员重新识别的准确性至关重要。所以，有必要对人的重新识别中的度量学习方法进行系统地回顾和总结，以期为度量学习的研究者提供参考。在本文中，我们对过去五年的度量学习方法进行了全面的分析，可以归纳为三个方面：距离度量方法，度量学习算法和度量结果的重新排名。然后，我们比较了一些代表性度量学习方法的性能，并进行了深入讨论。最后，我们对人重新识别中度量学习的未来研究方向进行了展望。距离度量方法，度量学习算法以及度量结果的重新排名。然后，我们比较了一些代表性度量学习方法的性能，并进行了深入讨论。最后，我们对人重新识别中度量学习的未来研究方向进行了展望。距离度量方法，度量学习算法以及度量结果的重新排名。然后，我们比较了一些代表性度量学习方法的性能，并进行了深入讨论。最后，我们对人重新识别中度量学习的未来研究方向进行了展望。