多视角相机协同提升眼神定位精度，解决遮挡与视野外目标难题。
Multi-view Gaze Target Estimation
- 融合双视角图像，通过头信息聚合与跨视图背景注意力增强估计
- 在第二视角人脸清晰时，性能显著超越单视角基线方法
- 可仅用另一视角图像推断第一视角眼神目标，适用于无直接视线场景
本文提出一种基于多视角相机的眼神目标估计方法。该方法以一对相机视图为输入，引入头信息聚合（HIA）模块融合双视角头部信息，提升估计精度；设计基于不确定性的目光选择（UGS）模块，筛选最可靠的眼动输出；并采用基于对极几何的场景注意力（ESA）模块，在不同视角间共享背景信息。实验表明，当第二视角能清晰捕捉人脸时，本方法显著优于单视角基线。更重要的是，该方法可仅凭第二视角图像，实现对第一视角眼神目标的估计，这是传统单视角方法无法做到的。论文还构建了一个用于多视角眼神目标估计的新数据集，代码与数据已公开于https://www3.cs.stonybrook.edu/~cvl/multiview_gte.html。
原文摘要 · Abstract (English)
This paper presents a method that utilizes multiple camera views for the gaze target estimation (GTE) task. The approach integrates information from different camera views to improve accuracy and expand applicability, addressing limitations in existing single-view methods that face challenges such as face occlusion, target ambiguity, and out-of-view targets. Our method processes a pair of camera views as input, incorporating a Head Information Aggregation (HIA) module for leveraging head information from both views for more accurate gaze estimation, an Uncertainty-based Gaze Selection (UGS) for identifying the most reliable gaze output, and an Epipolar-based Scene Attention (ESA) module for cross-view background information sharing. This approach significantly outperforms single-view baselines, especially when the second camera provides a clear view of the person's face. Additionally, our method can estimate the gaze target in the first view using the image of the person in the second view only, a capability not possessed by single-view GTE methods. Furthermore, the paper introduces a multi-view dataset for developing and evaluating multi-view GTE methods. Data and code are available at https://www3.cs.stonybrook.edu/~cvl/multiview_gte.html
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