Abstract
We introduce AG-VPReID, a challenging large-scale benchmark dataset for aerial-ground video-based person re-identification (ReID), comprising 6,632 identities, 32,321 tracklets, and 9.6 million frames captured from drones (15-120m altitude), CCTV, and wearable cameras. This dataset presents a real-world benchmark to investigate the robustness of Person ReID approaches against the unique challenges of cross-platform aerial-ground settings. To address these challenges, we propose AG-VPReID-Net, an end-to-end framework combining three complementary streams: (1) an Adapted Temporal-Spatial Stream addressing motion pattern inconsistencies and temporal feature learning, (2) a Normalized Appearance Stream using physics-informed techniques to tackle resolution and appearance changes, and (3) a Multi-Scale Attention Stream handling scale variations across drone altitudes. Our approach integrates complementary visual-semantic information from all streams to generate robust, viewpoint-invariant person representations. Extensive experiments demonstrate that AG-VPReID-Net outperforms state-of-the-art approaches on both our new dataset and other existing video-based ReID benchmarks, showcasing its effectiveness and generalizability. The relatively lower performance of all state-of-the-art approaches, including our proposed approach, on our new dataset highlights its challenging nature. The AG-VPReID dataset, code and models are available at this https URL.
Abstract (translated)
我们介绍了AG-VPReID，这是一个用于基于空中和地面视频的人重识别（ReID）的具有挑战性的大规模基准数据集。该数据集包含6,632个身份标识、32,321条轨迹片段以及从无人机（高度为15至120米）、闭路电视（CCTV）和可穿戴相机捕捉到的960万帧图像。此数据集提供了一个现实世界的基准，用于研究跨平台空地设置下的人重识别方法的鲁棒性。 为了应对这些挑战，我们提出了AG-VPReID-Net，这是一种端到端框架，结合了三个互补的数据流：（1）适应的时间空间流，以解决运动模式不一致和时间特征学习的问题；（2）采用物理启发技术来处理分辨率和外观变化的标准化外观流；以及（3）用于处理不同无人机高度下的尺度变化的多尺度注意力流。我们的方法整合了所有数据流中的互补视觉语义信息，生成鲁棒且视角不变的人体表示。 广泛的实验表明，在我们新的数据集和其他现有的基于视频的ReID基准测试上，AG-VPReID-Net的表现均优于最先进的方法，证明了其有效性和泛化能力。所有包括我们在内的最新方法在新数据集上的相对较低性能突显了该数据集的挑战性。 AG-VPReID数据集、代码和模型可在此网址获取：[此URL]（请将此处的占位符替换为实际的访问链接）。
URL
https://arxiv.org/abs/2503.08121
