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ARXIV:2603.09070 · UAV TRAJECTORY ESTIMATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09070UAV TRAJECTORY ESTIMATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition.
Opportunity summary
Pain A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition. In this work, we present a novel framework that derives UAV 3D trajectories and category information directly from Internet-scale…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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,…
UAV Trajectory Estimation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition.
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Paper Pack
10.48550/arXiv.2603.09070A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition.
Abstract
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.
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What was readable
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Dimensions overall score 7.0
PROBLEM
A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition. In this work, we present a novel framework that derives UAV 3D trajectories and category information directly from Internet-scale UAV videos, without relying...
METHOD
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 de...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
WHY NOW
UAV Trajectory Estimation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
UAV Trajectory Estimation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for estimating and classifying UAV trajectories from internet videos using language-driven data acquisition.
Segment
UAV Trajectory Estimation
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Commercial read
7.0/10 public viability
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