The paper presents a validated protocol for adapting drone‑based crowd‑counting models to the extreme conditions expected at the 2034 FIFA World Cup in Saudi Arabia. Using 525 controlled runs and a full‑resolution corpus, the authors demonstrate that label‑free adaptation can recover 31‑49% of shift‑induced error across multiple corruptions and severities, achieving a 41.8 MAE improvement over a frozen source model. They also introduce a severity law, a stability budget, and a flux‑based risk module that detects real congestion episodes, culminating in a six‑point deployment protocol for safe aerial crowd monitoring.
arXiv:2607. 01025v1 Announce Type: cross Abstract: Radio-frequency (RF) sensing is a central modality for counter-unmanned-aerial-system (counter-UAS) defence because it exploits the control, telemetry, and video links between a drone and its operator.
arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.
arXiv:2609.15579v1 Announce Type: new Abstract: An airborne sensor on a contested link cannot send video, so the appealing move is to send findings instead and report the ratio between the two. We ev...
arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).
arXiv:2609.37465v1 Announce Type: new Abstract: Counting completed wingbeats requires identifying individual cycles, including during frequency changes and pauses; estimating a dominant frequency alo...
SNF-Bench is an evaluation framework for long‑horizon fixed‑camera video generation that separates static background fidelity from dynamic flow persistence and drift leakage. It reports these three factors independently, using controlled injections of translation, rotation, scale drift, and progressive freezing to validate each metric’s sensitivity. Auditing public checkpoints shows that whole‑frame motion metrics can mislead, while SNF‑Bench reveals the true trade‑offs between motion quality and background stability.
The paper audits the Duckworth‑Lewis‑Stern (DLS) method, the standard for revising cricket scores after rain, using 8,150 international matches to generate 233,550 synthetic interruption scenarios. It finds two structured biases: a 137‑run prediction error range across match‑state buckets and a gender‑differential bias in ODIs, with women’s scores over‑predicted by an average of +7.63 runs versus +1.51 runs for men. The authors benchmark DLS against five modern machine‑learning models and introduce DLS‑Cal, a lightweight calibration layer that reduces overall bias by 31% in ODIs and 19% in T20Is, and a gender‑aware variant that nearly eliminates the residual bias for women’s ODIs.
The paper investigates how different velocity regimes affect broadcast‑viewport basketball analytics. Using a calibrated off‑screen imputation protocol, it compares four velocity settings—none, viewport‑legal observed, true‑for‑visible, and true‑for‑all—against a velocity‑aware ground truth across three analytical layers. Results show that velocity is largely useless for imputation, modestly useful for the control surface, and minimally useful for final team verdicts, with the visible channel providing the most benefit.
arXiv:2609.14560v1 Announce Type: new Abstract: General-purpose object detectors lose accuracy on UAV footage, where targets span only a handful of pixels and onboard compute is limited. Prior work c...
arXiv:2609.15562v1 Announce Type: cross Abstract: As generative models continue to advance, AI-generated content (AIGC) is becoming increasingly realistic, weakening the artifact cues commonly exploi...
arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.
arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.