扩展全球最大远距离全身生物识别数据集，支持真实场景下超远距识别。
Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
- 构建多源数据集，涵盖极端距离与复杂视角下的全身生物特征。
- 包含4个采集批次，覆盖城市街景、无人机与高空摄像头等真实环境。
- 适合研究远距离行人识别、安防监控与无人系统视觉算法的团队。
近年来，生物识别算法与应用系统在复杂环境和消费场景中取得了显著进展，实现了高精度与强鲁棒性。然而，在非传统场景下（如超远距离识别、建筑物上或无人机搭载摄像头拍摄）仍面临巨大挑战。本文总结了目前规模最大、专注于应对此类操作难题的数据集——BRIAR 的扩展版本，详细描述其数据构成、采集方法、数据清洗及标注流程。该扩展涵盖收集1-4批次，覆盖真实城市环境中的多种视角与距离条件，为远距离全身生物识别研究提供了重要资源。
原文摘要 · Abstract (English)
The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation.
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