Abstract
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 this https URL
Abstract (translated)
本文提出了一种利用多个摄像头视角进行注视目标估计（GTE）任务的方法。该方法整合了不同视角的信息，以提高准确性并扩展适用性，解决了现有单视图方法在面对诸如面部遮挡、目标模糊性和超出视野的目标等挑战时的局限性。我们的方法将一对相机视图作为输入，包括了一个头部信息聚合（HIA）模块，用于从两个视角利用头部信息进行更精确的注视估计；一个基于不确定性的注视选择（UGS），用以识别最可靠的注视输出；以及一个基于极线的场景注意力（ESA）模块，用于跨视图共享背景信息。此方法显著优于单视图基线，在第二个摄像头提供清晰面部视角的情况下尤为突出。此外，我们的方法能够仅通过第二视角中的人脸图像来估计第一视角中的注视目标，这是单视图GTE方法所不具备的能力。另外，本文还介绍了一个用于开发和评估多视图GTE方法的多视图数据集。数据和代码可在以下链接获取：[此 URL]
URL
https://arxiv.org/abs/2508.05857
