Fast pupil tracking based on the study of a boundary-stepped image model and multidimensional optimization Hook-Jives method

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yu. A. Grushko, R. Parovik
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引用次数: 2

Abstract

A new fast method for pupil detection and eyetracking real time is being developed based on the study of a boundary-step model of a grayscale image by the Laplacian-Gaussian operator and finding a new proposed descriptor of accumulated differences (point identifier), which displays a measure of the equidistance of each point from the boundaries of some relative monotonous area (for example, the pupil of the eye). The operation of this descriptor is based on the assumption that the pupil in the frame is the most rounded monotonic region with a high brightness difference at the border, the pixels of the region should have an intensity less than a predetermined threshold (but the pupil may not be the darkest region in the image). Taking into account all of the above characteristics of the pupil, the descriptor allows achieving high detection accuracy of its center and size, in contrast to methods based on threshold image segmentation, based on the assumption of the pupil as the darkest area, morphological methods (recursive morphological erosion), correlation or methods that investigate only the boundary image model (Hough transform and its variations with two-dimensional and three-dimensional parameter spaces, the Starburst algorithm, Swirski, RANSAC, ElSe). The possibility of representing the pupil tracking problem as a multidimensional unconstrained optimization problem and its solution by the Hook-Jeeves non-gradient method, where the function expressing the descriptor is used as the objective function, is investigated. In this case, there is no need to calculate the descriptor for each point of the image (compiling a special accumulator function), which significantly speeds up the work of the method. The proposed descriptor and method were analyzed, and a software package was developed in Python 3 (visualization) and C ++ (tracking kernel) in the laboratory of the Physics and Mathematics Faculty of Kamchatka State University of Vitus Bering, which allows illustrating the work of the method and tracking the pupil in real time.
基于边界阶跃图像模型和多维优化Hook-Jives方法的快速瞳孔跟踪研究
基于拉普拉斯-高斯算子对灰度图像的边界阶跃模型的研究,提出了一种新的累积差分描述符(点标识符),该描述符显示了每个点与某些相对单调区域(例如瞳孔)边界的等距度量,从而开发了一种新的快速瞳孔检测和眼球实时跟踪方法。该描述符的操作是基于这样的假设,即帧中的瞳孔是最圆角的单调区域,在边界处具有较高的亮度差,该区域的像素的强度应小于预定的阈值(但瞳孔可能不是图像中最暗的区域)。考虑到瞳孔的上述所有特征,与基于阈值图像分割的方法、基于瞳孔作为最暗区域的假设、形态学方法(递归形态学侵蚀)、相关性或仅研究边界图像模型的方法(霍夫变换及其随二维和三维参数空间的变化)相比,描述符可以实现对其中心和大小的高检测精度。Starburst算法,swiski, RANSAC, ElSe)。研究了将瞳孔跟踪问题表示为多维无约束优化问题的可能性,并采用Hook-Jeeves非梯度方法求解,其中描述子的函数作为目标函数。在这种情况下,不需要为图像的每个点计算描述符(编译一个特殊的累加器函数),这大大加快了方法的工作速度。对所提出的描述符和方法进行了分析,并在堪察加州立大学维图斯白令分校物理与数学学院实验室用Python 3(可视化)和c++(跟踪内核)开发了一个软件包,用于说明该方法的工作原理和实时跟踪瞳孔。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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