🤖 AI Summary
This work addresses the lack of publicly shareable video benchmarks for construction sites, particularly for rare, hazardous, and privacy-sensitive relational risks such as “workers under suspended loads.” To bridge this gap, the authors introduce SynthSite, a synthetic video benchmark comprising 55 clips that encompass diverse load configurations and surveillance conditions. They propose a structure-preserving blurring strategy that effectively suppresses worker identity while retaining critical geometric and spatiotemporal relationships, thereby balancing privacy protection with hazard recognition. Experimental results demonstrate that this approach significantly outperforms appearance-smoothing baselines, maintaining high-risk detection performance across five privacy-preserving conditions. Furthermore, the study reveals that preserving only raw visual appearance is insufficient to ensure alignment with human annotations, advocating for a paradigm shift in privacy evaluation from mere appearance obfuscation toward semantic structure preservation.
📝 Abstract
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 .