Person re-identification (Re-ID) is a key challenge in computer vision, requiring the matching of individuals across different cameras, locations, and time periods. While most research focuses on short-term scenarios with minimal appearance changes, real-world applications demand robust Re-ID systems capable of handling long-term scenarios, where persons' appearances can change significantly due to variations in clothing and physical characteristics. In this paper, we present CHIRLA, Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis, a novel dataset specifically designed for long-term person Re-ID. CHIRLA consists of recordings from strategically placed cameras over a seven-month period, capturing significant variations in both temporal and appearance attributes, including controlled changes in participants' clothing and physical features. The dataset includes 22 individuals, four connected indoor environments, and seven cameras. We collected more than five hours of video that we semi-automatically labeled to generate around one million bounding boxes with identity annotations. By introducing this comprehensive benchmark, we aim to facilitate the development and evaluation of Re-ID algorithms that can reliably perform in challenging, long-term real-world scenarios.

翻译：行人重识别（Re-ID）是计算机视觉领域的关键挑战，需要在不同摄像头、不同地点和不同时间段内对个体进行匹配。尽管大多数研究集中于外观变化极小的短期场景，但实际应用需要能够处理长期场景的鲁棒重识别系统，其中人员外观可能因服装和身体特征的改变而发生显著变化。本文提出CHIRLA（面向大规模分析的综合高分辨率身份识别与重识别），这是一个专为长期行人重识别设计的新型数据集。CHIRLA包含七个月内通过战略部署的摄像头采集的录像，捕捉了时间和外观属性的显著变化，包括参与者服装和身体特征的受控改变。该数据集涵盖22个个体、四个相互连通的室内环境以及七个摄像头。我们采集了超过五小时的视频，通过半自动标注生成了约一百万个带身份注释的边界框。通过引入这一综合性基准，我们旨在促进能够在具有挑战性的长期现实场景中可靠运行的重识别算法的开发与评估。