Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different cameras, it is currently an unsolved problem. In machine learning-based computer vision applications with reduced data sets, one possibility to improve the performance of re-identification system is through the augmentation of the set of images or videos available for training the neural models. Currently, one of the most robust ways to generate synthetic information for data augmentation, whether it is video, images or text, are the generative adversarial networks. This article reviews the most relevant recent approaches to improve the performance of person re-identification models through data augmentation, using generative adversarial networks. We focus on three categories of data augmentation approaches: style transfer, pose transfer, and random generation.

翻译：近年来，基于自动行人重识别系统的研究兴趣显著增长，主要应用于监控系统和智能商店软件的开发。由于行人姿态的多变性、不同光照条件、遮挡场景以及不同摄像头获取图像质量较差等因素，这一问题目前仍未得到解决。在基于机器学习的计算机视觉应用中，当数据集规模较小时，提升重识别系统性能的一种可行途径是通过扩充用于训练神经模型的图像或视频集合。当前，用于数据增强的合成信息生成（包括视频、图像或文本）最稳健的方法之一便是生成对抗网络。本文综述了近年来通过数据增强技术提升行人重识别模型性能的最相关方法，重点借助生成对抗网络实现。我们聚焦于三类数据增强方法：风格迁移、姿态迁移和随机生成。