为警务人员设计多机器人视频分析工具，提升应急响应效率。
Designing Multi-Robot Ground Video Sensemaking with Public Safety Professionals
- 构建首个多机器人地面视频理解测试平台，含38个真实事件场景。
- 开发MRVS系统，用大模型自动生成视频解释，减轻人工负担。
- 适合公共安全领域研究者与智能安防系统开发者参考。
地面机器人车队的视频可提升公共安全中的态势感知能力并减轻专业人员负担。然而，如何将多机器人视频整合进实际工作流程仍不清楚。我们与六家警察机构合作，开展两项研究。研究一提出首个多机器人地面视频理解测试平台，包含38个与公共安全相关的事件（EoI），20段机器人巡逻视频（10组昼夜配对），以及6项旨在改进现有视频分析实践的设计需求。研究二构建了MRVS工具，通过提示工程增强的大模型对多机器人巡逻视频流进行理解。参与者报告称，基于大模型的解释显著降低了手动工作量，并提升了判断信心，但同时也担忧误报和隐私问题。研究结论为未来多机器人视频理解工具的设计提供了重要启示。
原文摘要 · Abstract (English)
Videos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals' burden. Yet little is known about how to design and integrate multi-robot videos into public safety workflows. Collaborating with six police agencies, we examined how such videos could be made practical. In Study 1, we presented the first testbed for multi-robot ground video sensemaking. The testbed includes 38 events-of-interest (EoI) relevant to public safety, a dataset of 20 robot patrol videos (10 day/night pairs) covering EoI types, and 6 design requirements aimed at improving current video sensemaking practices. In Study 2, we built MRVS, a tool that augments multi-robot patrol video streams with a prompt-engineered video understanding model. Participants reported reduced manual workload and greater confidence with LLM-based explanations, while noting concerns about false alarms and privacy. We conclude with implications for designing future multi-robot video sensemaking tools.
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