发表机构
Government Technology Agency of Singapore（新加坡政府科技局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究悬吊负载下工人检测的隐私问题，引入SynthSite合成视频基准测试及隐私感知混合生成工作流程，在五种隐私条件下评估相关指标，发现保留结构的模糊处理效用更好，强调建筑安全分析隐私评估需兼顾外观抑制和几何线索保留。
AI 中文摘要
可公开共享的建筑视频基准测试仍然很少，特别是对于罕见、危险且难以发布的安全关键危险。我们研究悬吊负载下的工人，这是一种关系危险，它取决于工人与负载的几何形状和时间持续性，而不仅仅是目标检测。我们引入了SynthSite，这是一个包含55个片段的聚焦合成视频基准测试，涵盖各种负载配置、视角、杂波、遮挡和监控条件，以及一个隐私感知混合生成工作流程，支持可公开共享的基准测试创建和隐私受限的合成视频生成。然后，我们研究在不影响下游危险识别的情况下，是否可以抑制工人外观。在五种全身隐私条件下，我们评估了工人和负载保留、定位稳定性以及片段级危险识别。我们发现，保留结构的模糊处理比外观平滑基线保留了更多的下游效用，而且仅保留原始视觉参考并不能保证与人类危险标签的最强一致性。这些发现表明，建筑安全分析的隐私评估不仅应评估外观抑制，还应评估危险推理所需几何线索的保留。我们的数据集和代码可在这个https URL上获取。
英文摘要
Publicly shareable construction-video benchmarks remain scarce, especially for safety-critical hazards that are rare, dangerous to stage, and difficult to release. We study worker under suspended load, a relational hazard that depends on worker-load geometry and temporal persistence rather than object detection alone. We introduce SynthSite, a focused synthetic video benchmark of 55 clips spanning varied load configurations, viewpoints, clutter, occlusions, and surveillance conditions, together with a privacy-aware hybrid generation workflow that supports both publicly shareable benchmark creation and privacy-constrained synthetic video generation. We then ask whether worker appearance can be suppressed without undermining downstream hazard recognition. Under five whole-body privacy conditions, we evaluate worker and load retention, localization stability, and clip-level hazard recognition. We find that structure-preserving obfuscations retain substantially more downstream utility than appearance-smoothing baselines, and that preserving a raw visual reference alone does not guarantee the strongest agreement with human hazard labels. These findings suggest that privacy evaluation for construction safety analytics should assess not only appearance suppression, but also preservation of the geometric cues required for hazard reasoning. Our dataset and code are available at https://huggingface.co/datasets/govtech/SynthSite .
CommentsAccepted at the 3rd Workshop on Synthetic Data for Computer Vision (SynData4CV), CVPR 2026. OpenReview: https://openreview.net/forum?id=dMfhFmDWsg . Dataset and code: https://huggingface.co/datasets/govtech/SynthSite
