Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model's training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.

翻译：行人再识别任务旨在跨多个非重叠摄像头匹配行人。尽管监控领域中空中平台部署日益增多，现有行人再识别基准数据集主要聚焦于地-地匹配，而空-空匹配的研究极为有限。本文提出一个新的基准数据集AG-ReID，用于在空-地摄像头跨视角下执行行人再识别匹配任务。该数据集包含388个身份共21,983张图像，并为每个身份标注15个软属性。数据通过无人机（飞行高度15至45米）与校园地面闭路电视摄像头协同采集。由于不同摄像头视角下行人外观存在显著差异，本数据集对行人再识别提出了新颖的高空视角挑战。为应对该挑战，我们提出一种可解释算法，利用软属性引导行人再识别模型的训练。实验表明，该方法在空-地行人再识别任务中具有显著有效性。该数据集将公开发布，基线代码将在https://github.com/huynguyen792/AG-ReID开源，以促进该领域研究。