开源工具FIDAC可从视频中自动提取并分析人际距离，助力行为研究。
FIDAC: An Easy-to-use Pipeline to Extract and Interpret Interpersonal Distance From Video
- 融合多个面部检测模型，弥补单个模型缺陷，提升定位精度。
- 提供人脸选择人工编码与深度失真校准工具，减少测量偏差。
- 适合心理学、人机交互等领域研究者快速获取人际距离数据。
人与人之间的距离蕴含重要的相互感知信息，但此类信息难以从视频中直接提取和解读。我们开发了开源工具 Facial Interpersonal Distance Analysis and Coding (FIDAC)，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。该工具整合多个开源面部检测模型，有效弥补单一模型的不足。此外，还包含更精准的人脸跟踪方法，如人工编码人脸选择流程，以及用于降低深度失真影响的基准测试工具。未来计划评估FIDAC在不同深度和姿态下测量人际距离的有效性，并进一步集成亲缘学分析中的同步性等特征。
原文摘要 · Abstract (English)
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.
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