无标签无人机人群计数，精准预警大规模集会危险拥堵。
Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol, a Severity Law, and a Diagnostic for Label-Free Drone Crowd Counting, Toward the FIFA World Cup 2034 (Saudi Arabia)
- 基于无标签自适应技术，修复4种干扰下31%-49%的误差
- 在真实空域数据中实现48个单位的计数误差修复，大幅减少误报
- 适用于沙特世界杯等超大规模人群监控场景
沙特阿拉伯将举办2034年FIFA世界杯，已具备朝觐级人群管理能力。无人机计数需在训练数据未覆盖的视频中保持精度，且无需标签，同时能在踩踏发生前预警危险人流。本文基于525次受控实验、全分辨率数据集研究、五组伪证消融与五条件安全联锁评估，提出无标签自适应方法，在四种数据退化与五种严重程度下恢复31%-49%的漂移误差；最强模型相比冻结源模型提升41.8 MAE（95%置信区间[34.1, 49.6]，p=7.5×10⁻¹⁰，d=2.52）。建立严重性定律区分恒定误差边际与增长边际的方法，并提出稳定性预算以判断可飞行配置。在携带真实+48 MAE空中差距的全分辨率数据集上（源模型重训后验证MAE降至14.6，改善34%），自适应修复密集场景漏计问题，避免低估潜在踩踏风险；基于通量的风险模块在6段完整视频中成功触发2次真实拥堵事件。定位可修复误差：在支持物理守恒先验的300帧、200毫秒间隔（标准五倍）条件下，适应信号由归一化驱动而非流量驱动；连续性残差对比例计数误差域偏移保持不变，经四组开关消融相关性r=0.999，输入退化40%仅致精度下降0.05 MAE。无标签漂移门显示漂移幅度与精度损失排名无关（斯皮尔曼ρ=0.20；真实漂移中ρ=-0.60），量化出幅度门放弃58%余量。确立尾部监控为策略的无条件自适应，最终提出六步部署协议。
原文摘要 · Abstract (English)
Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy on footage unlike anything in its training corpus, without labels, and must warn of dangerous inflow before a crush forms. We deliver a validated answer built on 525 controlled runs, a full-resolution corpus study, five falsification ablations, and a five-condition safety-interlock evaluation. Label-free adaptation recovers 31-49% of shift-induced error across four corruptions and five severities, with the strongest method gaining 41.8 MAE over the frozen source (95% CI [34.1, 49.6], p=7.5x10^-10, d=2.52). We establish a severity law separating methods with a constant absolute margin from the one whose margin grows, and a stability budget identifying which configuration is safe to fly. On a full-resolution corpus carrying a genuine +48 MAE aerial gap (source retrained to 14.6 validation MAE, a 34% improvement), adaptation repairs the dense-scene undercounting that would otherwise under-report a forming crush, and the flux-based risk module fires on real congestion episodes in 2 of 6 full-length clips. We localise the recoverable error: in a regime built to favor a physics-informed conservation prior (300-frame clips at 200ms spacing, five times wider than standard), the adaptation signal is normalisation-driven, not flow-driven; the continuity residual is invariant to the proportional counting errors domain shift produces, confirmed by four on/off ablations correlated at r=0.999 and a 40% input corruption moving accuracy by only 0.05 MAE. A label-free shift gate shows shift magnitude and accuracy damage are rank-independent (Spearman rho=0.20; rho=-0.60 among genuine shifts), quantifying the 58% of headroom a magnitude gate forgoes. We establish unconditional adaptation with tail monitoring as policy, closing with a six-point protocol.
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