In this study, a novel framework for estimating a user's gaze point is proposed. If it is possible to detect what a user is looking at, appropriate services can be provided accordingly. Most existing methods for gaze estimation using image processing are classified into two types: those using the third-person viewpoint and those using the first-person viewpoint. However, the former approach lacks accurate estimation and the latter approach can cause privacy issues. In the proposed framework, sensor information from acceleration and gyro sensors installed in mobile devices is utilized instead of the first-person camera. From the images obtained from the third-person camera, a heatmap showing the possibility of objects that the user is looking at is estimated using machine learning techniques. This information is combined with the position of the user's head, which is obtained from the sensor information, to estimate the location of the user's gaze point. Experimental results show that the proposed method achieves a much higher accuracy than existing techniques. Obtaining user gaze information is very helpful in providing advanced location-based services (LBSs). The proposed framework can increase the added value of various types of LBSs.

中文翻译：

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用于估计基于位置的服务的注视点信息的框架


在这项研究中，提出了一种用于估计用户注视点的新颖框架。如果能够检测用户正在看什么，则可以相应地提供适当的服务。大多数现有的使用图像处理进行注视估计的方法分为两类：使用第三人称视点的方法和使用第一人称视点的方法。然而，前一种方法缺乏准确的估计，而后一种方法可能会导致隐私问题。在所提出的框架中，使用来自安装在移动设备中的加速度和陀螺仪传感器的传感器信息，而不是第一人称相机。根据从第三人称相机获得的图像，使用机器学习技术估计显示用户正在查看的对象的可能性的热图。该信息与从传感器信息获得的用户头部的位置相结合，以估计用户注视点的位置。实验结果表明，所提出的方法比现有技术具有更高的精度。获取用户注视信息对于提供先进的基于位置的服务（LBS）非常有帮助。所提出的框架可以增加各种类型的LBS的附加值。