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Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

机译:利用高清图像的双目瞳孔凝视点检测系统设计

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This study proposes a novel binocular pupil and gaze detection system utilizing a remote full high definition (full HD) camera and employing LabVIEW. LabVIEW is inherently parallel and has fewer time-consuming algorithms. Many eye tracker applications are monocular and use low resolution cameras due to real-time image processing difficulties. We utilized the computer’s direct access memory channel for rapid data transmission and processed full HD images with LabVIEW. Full HD images make easier determinations of center coordinates/sizes of pupil and corneal reflection. We modified the camera so that the camera sensor passed only infrared (IR) images. Glints were taken as reference points for region of interest (ROI) area selection of the eye region in the face image. A morphologic filter was applied for erosion of noise, and a weighted average technique was used for center detection. To test system accuracy with 11 participants, we produced a visual stimulus set up to analyze each eye’s movement. Nonlinear mapping function was utilized for gaze estimation. Pupil size, pupil position, glint position and gaze point coordinates were obtained with free natural head movements in our system. This system also works at 2046 × 1086 resolution at 40 frames per second. It is assumed that 280 frames per second for 640 × 480 pixel images is the case. Experimental results show that the average gaze detection error for 11 participants was 0.76° for the left eye, 0.89° for right eye and 0.83° for the mean of two eyes.
机译:这项研究提出了一种新颖的双眼瞳孔和注视检测系统,该系统利用远程全高清(full HD)摄像机并采用LabVIEW。 LabVIEW本质上是并行的,并且具有较少的耗时算法。由于实时图像处理的困难,许多眼动仪应用都是单眼的,并使用低分辨率相机。我们利用计算机的直接访问存储通道进行快速数据传输,并使用LabVIEW处理了全高清图像。全高清图像可以更轻松地确定瞳孔和角膜反射的中心坐标/大小。我们修改了相机,使相机传感器仅通过红外(IR)图像。闪光被用作面部图像中眼睛区域的感兴趣区域(ROI)区域选择的参考点。应用形态学滤镜来消除噪声,并使用加权平均技术进行中心检测。为了测试11位参与者的系统准确性,我们制作了视觉刺激程序来分析每只眼睛的运动。利用非线性映射函数进行注视估计。通过我们系统中自然的头部自由移动获得瞳孔大小,瞳孔位置,闪光位置和凝视点坐标。该系统还以2046×1086分辨率,每秒40帧的速度工作。假设情况是640×480像素图像每秒280帧。实验结果表明,11位参与者的平均凝视检测误差为左眼为0.76°,右眼为0.89°,而两只眼睛的平均值为0.83°。

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