Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline
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, t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2653 |
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| author | Linzhi Shang Chen Min Juan Wang Liang Xiao Dawei Zhao Yiming Nie |
| author_facet | Linzhi Shang Chen Min Juan Wang Liang Xiao Dawei Zhao Yiming Nie |
| author_sort | Linzhi Shang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-e65aec9ab055401ebe3328e3b5f14b21 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e65aec9ab055401ebe3328e3b5f14b212025-08-20T03:36:22ZengMDPI AGRemote Sensing2072-42922025-07-011715265310.3390/rs17152653Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and BaselineLinzhi Shang0Chen Min1Juan Wang2Liang Xiao3Dawei Zhao4Yiming Nie5Defense Innovation Institute, Beijing 100071, ChinaInstitute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaDefense Innovation Institute, Beijing 100071, ChinaDefense Innovation Institute, Beijing 100071, ChinaDefense Innovation Institute, Beijing 100071, ChinaVehicle 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%.https://www.mdpi.com/2072-4292/17/15/2653aerial-groundcross-viewremote sensingvehicle re-identification |
| spellingShingle | Linzhi Shang Chen Min Juan Wang Liang Xiao Dawei Zhao Yiming Nie Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline Remote Sensing aerial-ground cross-view remote sensing vehicle re-identification |
| title | Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline |
| title_full | Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline |
| title_fullStr | Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline |
| title_full_unstemmed | Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline |
| title_short | Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline |
| title_sort | aerial ground cross view vehicle re identification a benchmark dataset and baseline |
| topic | aerial-ground cross-view remote sensing vehicle re-identification |
| url | https://www.mdpi.com/2072-4292/17/15/2653 |
| work_keys_str_mv | AT linzhishang aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline AT chenmin aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline AT juanwang aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline AT liangxiao aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline AT daweizhao aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline AT yimingnie aerialgroundcrossviewvehiclereidentificationabenchmarkdatasetandbaseline |