Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers to develop and test many successful approaches. However, when such Re-ID models are deployed in new cities or environments, the task of searching for people within a network of security cameras is likely to face an important domain shift, thus resulting in decreased performance. Indeed, while most public datasets were collected in a limited geographic area, images from a new city present different features (e.g., people's ethnicity and clothing style, weather, architecture, etc.). In addition, the whole frames of the video streams must be converted into cropped images of people using pedestrian detection models, which behave differently from the human annotators who created the dataset used for training. To better understand the extent of this issue, this paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations. This method is used to benchmark four Re-ID approaches on three datasets, providing insight and guidelines that can help to design better Re-ID pipelines in the future.

人重新识别（RE-ID）旨在在相机网络中寻找感兴趣的人（查询）。在经典的重新设置中，查询查询在包含整个身体的正确裁剪图像的画廊中。最近，引入了实时重新ID设置，以更好地代表Re-ID的实际应用上下文。它包括在简短的视频中搜索查询，其中包含整个场景帧。最初的实时重新ID基线使用行人探测器来构建大型搜索库和经典的重新ID模型，以在画廊中找到查询。但是，产生的画廊太大，包含低质量的图像，从而降低了现场重新ID性能。在这里，我们提出了一种称为贸易的新现场重新ID方法，以产生较低的高质量画廊。贸易首先使用跟踪算法来识别画廊中同一个人的图像序列。随后，使用异常检测模型选择每个轨道的单个良好代表。贸易已在PRID-2011数据集的实时重新ID版本上进行了验证，并显示出比基线的显着改进。