# FPV-CRASH: A Dataset for Offline Crash Moment Localization in Drone First-Person-View Videos

arXiv: https://arxiv.org/abs/2610.19785 · HTML: https://mlsanity.com/paper/2610.19785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 29 of 29)

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- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
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- [Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching](https://mlsanity.com/paper/2311.12751/) (2023)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
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- [Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing](https://mlsanity.com/paper/2309.06837/) (2023)
- [ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos](https://mlsanity.com/paper/2604.09819/) (2026)
- [CloudTrack: Scalable UAV Tracking with Cloud Semantics](https://mlsanity.com/paper/2409.16111/) (2024)
- [Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles](https://mlsanity.com/paper/1907.00511/) (2019)
- [Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight](https://mlsanity.com/paper/2311.02667/) (2023)
- [INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators](https://mlsanity.com/paper/2210.09114/) (2022)
- [Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation](https://mlsanity.com/paper/2307.06101/) (2023)
- [RflyMAD: A Dataset for Multicopter Fault Detection and Health Assessment](https://mlsanity.com/paper/2311.11340/) (2023)
- [CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking](https://mlsanity.com/paper/2505.05936/) (2025)

## Abstract (arXiv)

Crash Moment Localization aims to localize the major physical impact that interrupts controlled flight in an onboard first-person-view (FPV) video recorded by a drone, thereby providing a temporal reference for retrospective crash analysis in aerial robotics. However, existing crash-video datasets predominantly target surveillance or dash-camera footage, and their annotation schemes and task formulations are not designed for temporal localization of the major physical impact in UAV onboard FPV videos. To address this gap, we present FPV-CRASH, a dataset and fixed-protocol reference evaluation for offline Crash Moment Localization in drone FPV videos. FPV-CRASH contains 1,052 drone FPV clips (526 Crash and 526 Normal); each Crash clip has a single-frame reference annotation for the Crash Moment, and every clip is paired with four English captions automatically generated from sampled frames and the known Crash/Normal label. Normal clips carry no timestamp target and provide training-time background supervision. The captions support an auxiliary Text-to-Video Retrieval task, whereas localization inference remains video-only. We further provide a heatmap-based reference model that estimates a continuous Crash Moment timestamp from sparsely sampled frames. Under the fixed, non-source-disjoint clip-level protocol, the reference model reduces MAE from 1.12 s for the strongest fixed timing-prior baseline to 0.56 s (a 50% relative reduction) and achieves a median absolute error (MedAE) of 0.33 s; the fraction of clips localized within 0.5 s rises from 37.14% to 65.71%. The release includes annotations, available source video identifiers and URLs, clipping metadata, fixed split lists, and preprocessing code so that the reportable portion can be reconstructed without redistributing source footage. At submission time, all source-compilation URLs remained accessible.