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 to facilitate research in this area.

翻译：行人重识别旨在跨多个非重叠摄像头匹配行人。尽管空中平台在监控领域的部署日益增多，但现有行人重识别基准主要关注地面-地面匹配，空中-空中匹配的研究十分有限。我们提出一个新的基准数据集——AG-ReID，该数据集在全新场景下执行行人重识别匹配：跨越空中和地面摄像头。该数据集包含388个身份的21,983张图像，并为每个身份标注了15个软属性。数据通过一架飞行高度在15至45米之间的无人机和校园内一个地面闭路电视摄像头采集。由于不同摄像头间行人外观存在显著差异，该数据集为行人重识别提出了新的高视点挑战。我们提出一种可解释算法，通过软属性引导行人重识别模型训练以应对该挑战。实验证明了该方法在空中-地面行人重识别任务中的有效性。该数据集将公开发布，基准代码将开源以促进该领域研究。