Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+, and Market1501. The evaluation codebase and results will be made publicly available for community use.

中文翻译：

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人员重新识别的系统评估和基准：特征、指标和数据集


人员重新识别 (re-id) 是安全和监控等视频分析应用中的一个关键问题。过去几年中，多个视觉算法数据集和代码的公开发布促进了该领域的快速进展。然而，由于采用了各种各样的特征、实验协议和评估指标，直接比较文献中报道的重识别算法变得很困难。为了满足这一需求，我们对单次和多次重识别算法进行了广泛的审查和性能评估。该实验协议融合了特征提取和度量学习方面的最新进展。为了确保公平比较，所有方法都是使用统一的代码库实现的，其中包括 11 种特征提取算法和 22 种度量学习和排名技术。除了 16 个其他公开可用的数据集之外，所有方法均使用密切模仿现实世界问题设置的新大型数据集进行评估：VIPeR、GRID、CAVIAR、DukeMTMC4ReID、3DPeS、PRID、V47、WARD、SAIVT-SoftBio、 CUHK01、CHUK02、CUHK03、RAiD、iLIDSVID、HDA+ 和 Market1501。评估代码库和结果将公开供社区使用。