Abstract
When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we face the main challenge of the significant appearance variations caused by different viewpoints, making identity matching difficult. To address this issue, previous methods attempt to reduce the differences between viewpoints by critical attributes and decoupling the viewpoints. While these methods can mitigate viewpoint differences to some extent, they still face two main issues: (1) difficulty in handling viewpoint diversity and (2) neglect of the contribution of local features. To effectively address these challenges, we design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method for the AGPReID task. The core of this framework relies on the Prompt Re-calibration Module (PRM), which adaptively re-calibrates prompts based on the input. Combined with the Local Feature Refinement Module (LFRM), SeCap can extract view-invariant features from local features for AGPReID. Meanwhile, given the current scarcity of datasets in the AGPReID field, we further contribute two real-world Large-scale Aerial-Ground Person Re-Identification datasets, LAGPeR and G2APS-ReID. The former is collected and annotated by us independently, covering $4,231$ unique identities and containing $63,841$ high-quality images; the latter is reconstructed from the person search dataset G2APS. Through extensive experiments on AGPReID datasets, we demonstrate that SeCap is a feasible and effective solution for the AGPReID task. The datasets and source code available on this https URL.
Abstract (translated)
在讨论空地人物再识别（AGPReID）任务时，我们面临的主要挑战是由于不同视角导致的显著外观变化，这使得身份匹配变得困难。为解决这一问题，以前的方法试图通过关键属性和解耦视角来减少视角之间的差异。尽管这些方法能够在一定程度上缓解视角差异的问题，但它们仍然面临着两个主要问题：（1）难以处理视角多样性；（2）忽视了局部特征贡献的作用。 为了有效应对这些挑战，我们设计并实现了一种名为自我校准与自适应提示（SeCap）的方法用于AGPReID任务。该框架的核心依赖于提示重新校准模块（PRM），它能够根据输入数据自适应地重新调整提示信息。结合局部特征精炼模块（LFRM），SeCap可以从局部特征中提取视角不变的特征，从而应用于AGPReID。 同时，在当前AGPReID领域的数据集稀缺的情况下，我们进一步贡献了两个大规模的真实世界空地人物再识别数据集：LAGPeR和G2APS-ReID。前者是我们独立收集并标注的数据集，包含4,231个独特的身份，并有63,841张高质量图像；后者是从人员搜索数据集G2APS重建而来。通过在AGPReID数据集上的广泛实验，我们证明了SeCap是解决AGPReID任务的可行且有效的方法。 这些数据集和源代码可以在提供的网址上获取：[此链接]（请将方括号内的内容替换为实际的数据集和代码发布页面URL）。
URL
https://arxiv.org/abs/2503.06965
