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.

翻译：最近,个人身份识别(Re-ID)受到了很多关注。大型数据集包括了各种个人的标签图像,让研究人员能够开发和测试许多成功的方法。然而,当这类重新身份识别模型在新的城市或环境中部署时,在安全摄像头网络中搜寻人员的任务可能面临重要的领域变化,从而导致性能下降。事实上,大多数公共数据集是在有限的地理区域内收集的,而来自一个新城市的图像则具有不同的特征(例如,人们的族裔和服装风格、天气、建筑等)。此外,整个视频流的框架必须转换成使用行人探测模型的人的作物图像,这些模型的行为与创建用于培训的数据集的人类说明者不同。为了更好地了解这一问题的范围,本文件介绍了评价重新身份识别方法和培训数据集的完整方法,以了解它们是否适合不严格地部署现场操作。这种方法用于为三个数据集的四种重新身份识别方法,提供洞察和准则,帮助今后设计更好的Re-ID管道。