Person re‐identification (re‐ID) has drawn attention significantly in the computer vision society due to its application and research significance. It aims to retrieve a person of interest across different camera views. However, there are still several factors that hinder the applications of person re‐ID. In fact, most common data sets either assume that pedestrians do not change their clothing across different camera views or are taken under constrained environments. Those constraints simplify the person re‐ID task and contribute to early development of person re‐ID, yet a person has a great possibility to change clothes in real life. To facilitate the research toward conquering those issues, this paper mainly introduces a new benchmark data set for person re‐identification. To the best of our knowledge, this data set is currently the most diverse for person re‐identification. It contains 107 persons with 9,738 images, captured in 15 indoor/outdoor scenes from September 2019 to December 2019, varying according to viewpoints, lighting, resolutions, human pose, seasons, backgrounds, and clothes especially. We hope that this benchmark data set will encourage further research on person re‐identification with clothes variation. Moreover, we also perform extensive analyses on this data set using several state‐of‐the‐art methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset.

中文翻译：

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人员重新识别中衣服变化的基准

行人重识别（re-ID）由于其应用和研究意义而在计算机视觉社会引起了极大的关注。它旨在在不同的相机视图中检索感兴趣的人。然而，仍然有几个因素阻碍了行人重识别的应用。事实上，大多数常见的数据集要么假设行人不会在不同的相机视图中改变他们的衣服，要么是在受限环境下拍摄的。这些限制简化了行人重新识别任务并有助于行人重新识别的早期发展，但一个人在现实生活中换衣服的可能性很大。为了促进克服这些问题的研究，本文主要介绍了一种新的行人再识别基准数据集。据我们所知，该数据集是目前最多样化的人员重新识别数据集。它包含 107 个人，9,738 张图像，从 2019 年 9 月到 2019 年 12 月在 15 个室内/室外场景中拍摄，根据视角、灯光、分辨率、人体姿势、季节、背景和衣服的不同而有所不同。我们希望这个基准数据集将鼓励进一步研究衣服变化的人重新识别。此外，我们还使用几种最先进的方法对该数据集进行了广泛的分析。我们的数据集可在 https://github.com/nkicsl/NKUP-dataset 获得。我们希望这个基准数据集将鼓励进一步研究衣服变化的人重新识别。此外，我们还使用几种最先进的方法对该数据集进行了广泛的分析。我们的数据集可在 https://github.com/nkicsl/NKUP-dataset 获得。我们希望这个基准数据集将鼓励进一步研究衣服变化的人重新识别。此外，我们还使用几种最先进的方法对该数据集进行了广泛的分析。我们的数据集可在 https://github.com/nkicsl/NKUP-dataset 获得。