EgoPolice：用于高风险警察随身摄像机视频中自我中心视频理解的基准
EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footage
AI总结：
研究旨在为高风险警察随身摄像机视频中的自我中心视频理解创建基准EgoPolice，通过精心挑选标注数据，设置分类和问答任务，对模型测试发现即使优秀模型预测高风险行动也有困难，该基准为开发相关模型及下游人工审查奠定基础。
摘要：
我们引入了EgoPolice，这是一个精心策划的真实自我中心警察与平民互动的数据集，源自公开的随身摄像机视频。我们选择了对警察行为研究至关重要的警察与平民行动标签，并以每秒的粒度进行注释。这些视频具有快速且不规则的相机运动、密集的人际互动和罕见的高风险事件，使其成为运动稳健和情境感知自我中心感知的具有挑战性的基准。我们提供了分类和多项选择题回答两项不同任务，并对开源和闭源模型进行基准测试。我们发现，即使是像Gemini 2.5 Pro这样最好的视频模型，在准确预测诸如“拿出武器”等高风险行动时仍存在困难。除了作为基准，EgoPolice还为开发能够识别大规模随身摄像机视频存储库中感兴趣事件的模型提供了基础，从而实现更高效的下游人工审查。
英文摘要：
We introduce EgoPolice, a carefully curated dataset of real, egocentric police-civilian interactions, sourced from publicly available body-worn camera videos. We select police-civilian action labels that are critical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular camera motion, dense human interactions, and rare high-stakes events, making the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classification and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video models like Gemini 2.5 Pro still struggle to accurately predict high-risk actions such as "Weapon Out". Beyond serving as a benchmark, EgoPolice provides a foundation for developing models capable of identifying events of interest in large-scale body-worn camera video repositories, enabling more efficient downstream human review.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
