Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark

[2503.06983] Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2503.06983 (cs) [Submitted on 10 Mar 2025 ( v1 ), last revised 7 Dec 2025 (this version, v2)] Title: Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark Authors: Jiahao Wang , Xiangyu Cao , Jiaru Zhong , Yuner Zhang , Zeyu Han , Haibao Yu , Chuang Zhang , Lei He , Shaobing Xu , Jianqiang Wang View a PDF of the paper titled Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark, by Jiahao Wang and 8 other authors View PDF HTML (experimental) Abstract: 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 this https URL . Comments: Accepted by AAAI 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO) Cite as: arXiv:2503.06983 [cs.CV] (or arXiv:2503.06983v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2503.06983 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 12, pp. 9867-9875 (2026) Related DOI : https://doi.org/10.1609/aaai.v40i12.37951 Focus to learn more DOI(s) linking to related resources Submission history From: Jiahao Wang [ view email ] [v1] Mon, 10 Mar 2025 07:00:07 UTC (4,381 KB) [v2] Sun, 7 Dec 2025 13:09:25 UTC (4,253 KB) Full-text links: Access Paper: View a PDF of the paper titled Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark, by Jiahao Wang and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-03 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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