言語モデルを用いたインターネット動画からの3D UAV軌跡推定と分類
3D UAV Trajectory Estimation and Classification from Internet Videos via Language Model
インターネット上のUAV動画から手動アノテーションなしで3D軌跡とカテゴリを推定するフレームワークを提案し、物理情報に基づく精緻化で軌跡の滑らかさと運動学的整合性を確保した。公開ベンチマークでのゼロショット転移実験により、データ量増加に伴う性能向上と実世界適用可能性を示した。
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著者: Haoxiang Lei, Daotong Wang, Shenghai Yuan, Jianbo Su
分類: cs.RO
原文アブストラクト
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.
