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New 2D pupil and spot center positioning technology under real — Time eye tracking

机译:实时眼追踪下新的2D瞳孔和点中心定位技术

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Eye tracking technology is an important technology in the field of artificial intelligence(AI). Eye tracking will promote the development of human-computer interaction(HCI). Spot Center Corneal Reflex (PCCR) is an eye tracking technique that relies on pupils and reflected light spots. Therefore, it is significant to accurately locate the pupil position and reflected spot position. The traditional algorithm used the edge and the gray information of the image to extract the contours of the pupil and the spot, and then determine the location through the fitting. However, the collected images will be affected by many environmental factors, the boundary point and the fitting calculation will greatly affect the efficiency and stability of the algorithm. In this paper, a new method combining image gradient information with threshold segmentation is proposed. Gradient detection and threshold segmentation are carried out in the region of interest, and the pupil and reflection spot are extracted directly. So, this paper use the centroid method to calculate the center coordinates more accurately. The algorithm has a good robust performance to avoid noise and environmental effects. The algorithm used to develop human eye tracking system to achieve real-time eye tracking, while ensuring accuracy.
机译:眼跟踪技术是人工智能(AI)领域的重要技术。眼睛跟踪将促进人机互动(HCI)的发展。现场中心角膜反射(PCCR)是一种依赖于瞳孔和反射光点的眼睛跟踪技术。因此,重要的是准确地定位瞳孔位置和反射点位置。传统算法使用了图像的边缘和灰色信息来提取瞳孔的轮廓和斑点,然后通过配件确定位置。然而,收集的图像将受到许多环境因素的影响,边界点和拟合计算将极大地影响算法的效率和稳定性。在本文中,提出了一种结合具有阈值分割的图像梯度信息的新方法。在感兴趣区域中进行梯度检测和阈值分割,直接提取瞳孔和反射点。因此,本文使用质心方法更准确地计算中心坐标。该算法具有良好的稳健性能,以避免噪声和环境效果。该算法用于开发人眼跟踪系统实现实时眼跟踪,同时确保精度。

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