发表机构
University of California, Berkeley; University of Florida; Stanford University（加利福尼亚大学伯克利分校; 佛罗里达大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何从视频中提取和解释人际距离，开发开源库FIDAC，合并多模型数据，包含精确跟踪方法，后续计划评估其在不同场景下测量人际距离的有效性并集成新特征。
AI 中文摘要
人与人之间的距离揭示了他们对彼此认知的重要信息。然而，此类信息不易从视频输入中提取和解释。我们开发了一个开源库——面部人际距离分析与编码（FIDAC），它将面部检测结果转化为关于位置和人际距离的可操作数据。该工具合并多个开源面部检测模型的数据，策略性地弥补单个模型的不足。此外，我们还包括更精确跟踪的方法，如用于面部选择的人工编码管道和减少深度失真的基准工具。下一步，我们计划通过评估其在不同深度和方向测量人际距离的有效性，并进一步将诸如同步性等空间分析特征集成到软件中，在FIDAC的基础上继续发展。
英文摘要
The distance between persons reveals significant information about their perception of each other. However, such information is not easily extractable and interpretable from video input. We developed an open-sourced library, Facial Interpersonal Distance Analysis and Coding (FIDAC) that transforms facial detection results into actionable data about location and interpersonal distance. This tool merges data from multiple open-source facial detection models, strategically compensating for gaps in any individual model. In addition, we include methods for more accurate tracking, such as a pipeline for human coding of the selection of faces and a benchmarking tool to reduce depth distortion. For next steps, we plan on building upon FIDAC by evaluating its effectiveness at measuring interpersonal distance at various depths and orientations while further integrating features of proxemic analysis such as synchrony into its software.
