面向多设备交互的眼动跟踪方法

摘要

Recently, more and more human-computer interaction applications are used cross multiple devices, but existed eye tracking approaches only support single device application, and do not meet the requirement of cross-device interaction. An eye tracking approach oriented to cross-device interaction was proposed. We com-bined pupil area detection and pupil center detection to reduce the difficulty of pupil image recognition. We pre-dicted position of the missing Purkinje image during the recognition process, and then computed the vector of pu-pil-Purkinje image. On the other hand, we utilized edge detection algorithm to solve device screen recognition problem, and then created the list of device screen coordinates. Later, we used the coordinates from the list to compute the shapes and sizes of the device screens and identified different devices accordingly. Then we com-puted the fixation coordinate based on vector of pupil-Purkinje image, and improved the accuracy of fixation co-ordinate computation with error compensation when there were errors caused by head movement. Finally, a head mounted eye tracker system named as MultiGaze was developed, and the test results indicated it had high accu-racy of fixation computation for cross-device interaction.%当前,越来越多的人机交互应用需要依靠多个设备共同完成,传统针对单个设备的眼动跟踪方法已很难适应多设备交互的需求。为此,提出一种面向多设备交互的眼动跟踪方法。针对用户眼球运动幅度显著变大给图像识别带来的影响,采用待选瞳孔区域和瞳孔中心识别相结合的方法识别瞳孔；同时对普洱钦斑位置进行预测,插补识别过程中丢失的普洱钦斑；在此基础上,建立瞳孔-普洱钦斑反射向量。另一方面,利用边缘检测方法识别设备屏幕,并通过建立不同设备屏幕的顶点位置列表,比较屏幕形状和面积以区分不同设备；再根据瞳孔-普洱钦斑反射向量进行眼动注视点坐标拟合计算,并结合头部运动误差补偿方法提高多设备之间眼动注视点坐标的计算精度。最后设计开发了头戴式眼动跟踪系统 MultiGaze,用户测试结果表明,文中方法在多设备交互环境下能有效地提高注视点计算精度。