Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911578919534592 |
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| author | Wang, Jiahao Cao, Xiangyu Zhong, Jiaru Zhang, Yuner Han, Zeyu Yu, Haibao Zhang, Chuang He, Lei Xu, Shaobing Wang, Jianqiang |
| author_facet | Wang, Jiahao Cao, Xiangyu Zhong, Jiaru Zhang, Yuner Han, Zeyu Yu, Haibao Zhang, Chuang He, Lei Xu, Shaobing Wang, Jianqiang |
| contents | While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is often impeded by significant economic barriers. Aerial-ground cooperation (AGC), which pairs ground vehicles with drones, presents a more economically viable and rapidly deployable alternative. However, this emerging field has been held back by a critical lack of high-quality public datasets and benchmarks. To bridge this gap, we present \textit{Griffin}, a comprehensive AGC 3D perception dataset, featuring over 250 dynamic scenes (37k+ frames). It incorporates varied drone altitudes (20-60m), diverse weather conditions, realistic drone dynamics via CARLA-AirSim co-simulation, and critical occlusion-aware 3D annotations. Accompanying the dataset is a unified benchmarking framework for cooperative detection and tracking, with protocols to evaluate communication efficiency, altitude adaptability, and robustness to communication latency, data loss and localization noise. By experiments through different cooperative paradigms, we demonstrate the effectiveness and limitations of current methods and provide crucial insights for future research. The dataset and codes are available at https://github.com/wang-jh18-SVM/Griffin. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark Wang, Jiahao Cao, Xiangyu Zhong, Jiaru Zhang, Yuner Han, Zeyu Yu, Haibao Zhang, Chuang He, Lei Xu, Shaobing Wang, Jianqiang Computer Vision and Pattern Recognition Robotics While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is often impeded by significant economic barriers. Aerial-ground cooperation (AGC), which pairs ground vehicles with drones, presents a more economically viable and rapidly deployable alternative. However, this emerging field has been held back by a critical lack of high-quality public datasets and benchmarks. To bridge this gap, we present \textit{Griffin}, a comprehensive AGC 3D perception dataset, featuring over 250 dynamic scenes (37k+ frames). It incorporates varied drone altitudes (20-60m), diverse weather conditions, realistic drone dynamics via CARLA-AirSim co-simulation, and critical occlusion-aware 3D annotations. Accompanying the dataset is a unified benchmarking framework for cooperative detection and tracking, with protocols to evaluate communication efficiency, altitude adaptability, and robustness to communication latency, data loss and localization noise. By experiments through different cooperative paradigms, we demonstrate the effectiveness and limitations of current methods and provide crucial insights for future research. The dataset and codes are available at https://github.com/wang-jh18-SVM/Griffin. |
| title | Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2503.06983 |