3D UAV Trajectory Estimation and Classification from Internet Videos via Language Model
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arXiv
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| Format: | Preprint |
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2026
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| author | Lei, Haoxiang Wang, Daotong Yuan, Shenghai Su, Jianbo |
| author_facet | Lei, Haoxiang Wang, Daotong Yuan, Shenghai Su, Jianbo |
| contents | Reliable 3D trajectory estimation of unmanned aerial vehicles (UAVs) is a fundamental requirement for anti-UAV systems, yet the acquisition of large-scale and accurately annotated trajectory data remains prohibitively expensive. In this work, we present a novel framework that derives UAV 3D trajectories and category information directly from Internet-scale UAV videos, without relying on manual annotations. First, language-driven data acquisition is employed to autonomously discover and collect UAV-related videos, while vision-language reasoning progressively filters task-relevant segments. Second, a training-free cross-modal label generation module is introduced to infer 3D trajectory hypotheses and UAV type cues. Third, a physics-informed refinement process is designed to impose temporal smoothness and kinematic consistency on the estimated trajectories. The resulting video clips and trajectory annotations can be readily utilized for downstream anti-UAV tasks. To assess effectiveness and generalization, we conduct zero-shot transfer experiments on a public, well-annotated 3D UAV benchmark. Results reveal a clear data scaling behavior: as the amount of online video data increases, zero-shot transfer performance on the target dataset improves consistently, without any target-domain training. The proposed method closely approaches the current state-of-the-art, highlighting its robustness and applicability to real-world anti-UAV scenarios. Code and datasets will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09070 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 3D UAV Trajectory Estimation and Classification from Internet Videos via Language Model Lei, Haoxiang Wang, Daotong Yuan, Shenghai Su, Jianbo Robotics Reliable 3D trajectory estimation of unmanned aerial vehicles (UAVs) is a fundamental requirement for anti-UAV systems, yet the acquisition of large-scale and accurately annotated trajectory data remains prohibitively expensive. In this work, we present a novel framework that derives UAV 3D trajectories and category information directly from Internet-scale UAV videos, without relying on manual annotations. First, language-driven data acquisition is employed to autonomously discover and collect UAV-related videos, while vision-language reasoning progressively filters task-relevant segments. Second, a training-free cross-modal label generation module is introduced to infer 3D trajectory hypotheses and UAV type cues. Third, a physics-informed refinement process is designed to impose temporal smoothness and kinematic consistency on the estimated trajectories. The resulting video clips and trajectory annotations can be readily utilized for downstream anti-UAV tasks. To assess effectiveness and generalization, we conduct zero-shot transfer experiments on a public, well-annotated 3D UAV benchmark. Results reveal a clear data scaling behavior: as the amount of online video data increases, zero-shot transfer performance on the target dataset improves consistently, without any target-domain training. The proposed method closely approaches the current state-of-the-art, highlighting its robustness and applicability to real-world anti-UAV scenarios. Code and datasets will be released upon acceptance. |
| title | 3D UAV Trajectory Estimation and Classification from Internet Videos via Language Model |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.09070 |