视线跟踪系统中的分级瞳孔定位算法

 基于视线跟踪的人机交互技术具有便捷性和快速性,可实现视线与计算机交互的目的。基于头戴式头盔视线跟踪系统,提出了一种分级瞳孔定位方法。为消除各种干扰因素对瞳孔定位的影响,首先利用图像二值化方法获取瞳孔区域,并进行最小外接椭圆的计算,然后利用瞳孔区域与最小外接椭圆的交叠区域与瞳孔区域的面积比值,判断是否为完整瞳孔。如果是不完整的瞳孔,则利用粒子群优化(PSO)算法和椭圆周差分算子在图像中搜寻最优椭圆位置,将其作为最终的瞳孔轮廓。基于视线跟踪系统的实验结果表明,该方法兼顾了速度和精度,是实现复杂环境下瞳孔定位的一个有效策略。

The Classified Pupil Localization Algorithm of Line-of-sight Tracking System

The man-machine interaction approach based on line-of-sight tracking is characterized by promptness and celerity,and it can be used to achieve the interaction between human sight and computer.This paper builds one kind of helmet line-of-sight tracking system.A cascade pupil localization method is proposed in order to eliminate various obstruction factors.First,the pupil area is retrieved using its binary image and the minimum outer ellipse is calculated.Second,the overlapping portion of the pupil area and the minimum outer ellipse is retrieved and used to calculate the ratio of the overlapping area and pupil area. If the ratio is under the threshold value,the pupil is uncovered;otherwise it is covered.The particle swarm optimization(PSO) and ellipse difference are used to search the best pupil location.Experimental results show that our method is a fast and robust method,and an effective strategy for pupil localization in complex environments.