IEEE Trans Neural Netw Learn Syst. 2019 Oct;30(10):3010-3023. doi: 10.1109/TNNLS.2018.2865525. Epub 2018 Sep 3.
Gaze estimation, which aims to predict gaze points with given eye images, is an important task in computer vision because of its applications in human visual attention understanding. Many existing methods are based on a single camera, and most of them only focus on either the gaze point estimation or gaze direction estimation. In this paper, we propose a novel multitask method for the gaze point estimation using multiview cameras. Specifically, we analyze the close relationship between the gaze point estimation and gaze direction estimation, and we use a partially shared convolutional neural networks architecture to simultaneously estimate the gaze direction and gaze point. Furthermore, we also introduce a new multiview gaze tracking data set that consists of multiview eye images of different subjects. As far as we know, it is the largest multiview gaze tracking data set. Comprehensive experiments on our multiview gaze tracking data set and existing data sets demonstrate that our multiview multitask gaze point estimation solution consistently outperforms existing methods.
注视估计旨在通过给定的眼部图像预测注视点，由于其在人类视觉注意力理解中的应用，因此它是计算机视觉中的一项重要任务。许多现有方法基于单个摄像机，而且大多数方法仅关注注视点估计或注视方向估计。在本文中，我们提出了一种使用多视角摄像机进行注视点估计的新的多任务方法。具体来说，我们分析了注视点估计和注视方向估计之间的密切关系，并使用部分共享卷积神经网络架构来同时估计注视方向和注视点。此外，我们还引入了一个新的多视角注视跟踪数据集，该数据集由不同主体的多视角眼部图像组成。据我们所知，这是最大的多视角注视跟踪数据集。在我们的多视角注视跟踪数据集和现有数据集上的综合实验表明，我们的多视角多任务注视点估计解决方案始终优于现有方法。
