Abstract
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.
Abstract (translated)
人员重识别（Re-ID）是计算机视觉领域中的一个关键挑战，它要求在不同摄像头、地点和时间段内匹配同一人物。尽管大多数研究集中在短期场景下，即外观变化较小的情况下，但实际应用场景需要能够处理长期情景的鲁棒性重识别系统，在这种情况下，由于穿着或身体特征的变化，人物的外观可能发生显著改变。本文中，我们介绍了CHIRLA（用于大规模分析的综合高分辨率身份和重识别），这是一个专门为长时间人员重识别设计的新数据集。CHIRLA包含了为期七个月、从战略性放置的七个摄像头收集的数据，记录了包括参与者服装和身体特征控制变化在内的时空属性的重大变化。该数据集包括22名个体、四个连接的室内环境以及七个摄像头。我们半自动标注超过五小时的视频，生成带有身份注释的大约一百万个边界框。通过引入这一全面基准测试，我们的目标是促进可靠地应用于挑战性的长时间现实场景中的重识别算法的发展和评估。
URL
https://arxiv.org/abs/2502.06681
