【摘要】 基于极限学习机(ELM)所具有的训练速度快、适合多分类的特点,提出一种新的单摄像机视线追踪系统视线落点估计方法.在初始标定阶段,将多视线参数作为ELM输入,将视线在屏幕上的落点区域作为输出,将非线性多项式作为激活函数,通过初始标定获取ELM训练数据,建立视线特征参数和视线屏幕落点之间的映射模型.实验结果表明,通过对不同角度分布的视线落点进行估计和改变隐层单元数量进行训练,基于ELM的视线落点估计方法无论视线落点精度还是稳定性均优于传统的非线性多项式拟合方法.更多还原

【Abstract】 A novel method of gaze point estimation used in single camera system,which has faster training speed and is suitable for multiple classifications,was proposed based on ELM.In the initial calibration phase,multi gaze parameters served as ELM input,while the gaze point area was ELM output.The nonlinear polynomial was used as activation function.ELM training data was obtained through the initial calibration,and then the mapping model between the line of sight parameters and the gaze point was established.Through estimation of gaze point distribution of different angles and changing the number of hidden neurons,it was found that the accuracy and stability of gaze point obtained by ELM method are better than those obtained by traditional nonlinear polynomial model.更多还原