Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline
文献类型：期刊论文
| 作者 | Shang, Linzhi1; Min, Chen2; Wang, Juan3; Xiao, Liang1; Zhao, Dawei1; Nie, Yiming1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-07-31 |
| 卷号 | 17期号:15页码:21 |
| 关键词 | aerial-ground cross-view remote sensing vehicle re-identification |
| DOI | 10.3390/rs17152653 |
| 英文摘要 | Vehicle re-identification (Re-ID) is a critical computer vision task that aims to match the same vehicle across spatially distributed cameras, especially in the context of remote sensing imagery. While prior research has primarily focused on Re-ID using remote sensing images captured from similar, typically elevated viewpoints, these settings do not fully reflect complex aerial-ground collaborative remote sensing scenarios. In this work, we introduce a novel and challenging task: aerial-ground cross-view vehicle Re-ID, which involves retrieving vehicles in ground-view image galleries using query images captured from aerial (top-down) perspectives. This task is increasingly relevant due to the integration of drone-based surveillance and ground-level monitoring in multi-source remote sensing systems, yet it poses substantial challenges due to significant appearance variations between aerial and ground views. To support this task, we present AGID (Aerial-Ground Vehicle Re-Identification), the first benchmark dataset specifically designed for aerial-ground cross-view vehicle Re-ID. AGID comprises 20,785 remote sensing images of 834 vehicle identities, collected using drones and fixed ground cameras. We further propose a novel method, Enhanced Self-Correlation Feature Computation (ESFC), which enhances spatial relationships between semantically similar regions and incorporates shape information to improve feature discrimination. Extensive experiments on the AGID dataset and three widely used vehicle Re-ID benchmarks validate the effectiveness of our method, which achieves a Rank-1 accuracy of 69.0% on AGID, surpassing state-of-the-art approaches by 2.1%. |
| 资助项目 | Defense Innovation Institution |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001549662100001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41748]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhao, Dawei |
| 作者单位 | 1.Def Innovat Inst, Beijing 100071, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shang, Linzhi,Min, Chen,Wang, Juan,et al. Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline[J]. REMOTE SENSING,2025,17(15):21. |
| APA | Shang, Linzhi,Min, Chen,Wang, Juan,Xiao, Liang,Zhao, Dawei,&Nie, Yiming.(2025).Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline.REMOTE SENSING,17(15),21. |
| MLA | Shang, Linzhi,et al."Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline".REMOTE SENSING 17.15(2025):21. |
入库方式： OAI收割
来源：计算技术研究所
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