构建百万级标注的长期跨摄像头行人重识别数据集
CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
- 七个月多相机采集，覆盖真实衣物与外貌变化
- 含22人、超5小时视频、近100万带标注框
- 专为长时序、遮挡、多摄像头场景设计基准
行人重识别（Re-ID）是计算机视觉中的关键挑战，需在不同摄像头、位置和时间匹配同一人。现有研究多关注短期场景，但实际应用需应对长期衣物与体貌变化。本文提出CHIRLA：面向大规模分析的高分辨率综合识别与重识别数据集。该数据集在四组相连的室内环境中，通过七个部署相机持续七个月采集，记录真实移动行为，包含显著的服装与外观变化。数据集涵盖22名个体，超过五小时视频，约100万带身份标注的边界框，采用半自动方式标注。同时定义了针对行人跟踪与重识别的基准评测协议，涵盖遮挡、重新出现及多摄像头等复杂场景。通过此综合性基准，旨在推动能在真实长期场景中稳定运行的重识别算法研发与评估。基准代码已公开于：https://github.com/bdager/CHIRLA。
原文摘要 · Abstract (English)
Person re-identification (Re-ID) is a key challenge in computer vision, requiring the matching of individuals across cameras, locations, and time. While most research focuses on short-term scenarios with minimal appearance changes, real-world applications demand robust systems that handle long-term variations caused by clothing and physical changes. We present CHIRLA, Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis, a novel dataset designed for video-based long-term person Re-ID. CHIRLA was recorded over seven months in four connected indoor environments using seven strategically placed cameras, capturing realistic movements with substantial clothing and appearance variability. The dataset includes 22 individuals, more than five hours of video, and about 1M bounding boxes with identity annotations obtained through semi-automatic labeling. We also define benchmark protocols for person tracking and Re-ID, covering diverse and challenging scenarios such as occlusion, reappearance, and multi-camera conditions. 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. The benchmark code is publicly available at: https://github.com/bdager/CHIRLA.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。