Abstract
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.
Abstract (translated)
人重发身份识别在不同角度的摄像头中进行匹配。尽管在监视领域的空中平台部署越来越多,但当前现有的人重发身份识别基准关注点是地面和空中的匹配,而且只有非常有限的努力涉及空中匹配。我们提出了一个新的基准数据集——AG-ReID,该数据集在一个新的环境中进行人重发身份识别匹配:在不同高度的空中和地面摄像头之间进行匹配。我们的数据集包含388个身份的21,983张照片和每个身份的15个软属性。这些数据由一架在15到45米高度飞行的无人机和在大学校园内的地面CCTV摄像头收集。我们的数据集由于这些摄像头之间的人外貌差异,提出了一种新的高度视角挑战,因此我们提出了一种可解释算法来指导人重发身份模型使用软属性进行训练,以解决这个挑战。实验表明,我们的方法对于空中地面人重发身份任务非常有效。数据集将发表,基准代码将开源,以促进这一领域的研究。
URL
https://arxiv.org/abs/2303.08597
