Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention

2026-08-25

AI Summary

本文提出一种基于互视注意力的可解释计算模型，用于分析社会互动，通过头部朝向估计和几何推理来衡量个体及群体参与度。

Abstract

Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable computational model of social engagement inspired by psychological theories of mutual visual attention. Rather than learning interaction patterns end-to-end, our framework explicitly models dyadic visual attention and aggregates these cues into interpretable measures of individual and group engagement. The resulting modular framework combines state-of-the-art head orientation estimation with lightweight geometric reasoning, producing explanations that remain accessible to non-technical users. We evaluate the proposed approach on a variety of data through quantitative experiments and demonstrate its practical usefulness with qualitative visualizations designed to support teachers, caregivers, and social workers in understanding group interaction dynamics.

Problem

• social interactions
• non-verbal visual data
• behavior analysis
• activity monitoring

Innovation

• interpretable computational model
• mutual visual attention
• dyadic visual attention
• head orientation estimation