基于机器学习的头部自由视线追踪方法及其在电动病床端的应用

Machine learning-based free-head gaze tracking method and its application on the electric sickbed

头部自由的三维视线追踪具有重要的意义。针对传统的视线追踪方法存在的精度低、设备复杂和穿戴受限等问题,提出了一种基于机器学习的单目头部自由的三维视线追踪技术,构建出了两个轻量、高精度、实时的视线追踪模型,分别可进行注视点估计和注视方向估计。注视点估计模型首先利用Dlib进行人脸特征点定位得到眼睛图像进而进行PnP解算得到头部姿态信息,将两种信息与部分特征点坐标共同输入到多通道卷积神经网络中,并最终估计得到人眼注视点。注视方向估计网络是注视点估计网络的简化版本。将本文所提出的视线追踪技术与电动病床相结合,构建出一套基于眼球驱动的电动病床系统,该系统允许患者利用眼睛控制电动病床的运作。实验结果表明:所提出的注视点模型在MPIIGaze数据集上的误差为4.1 cm,注视方向估计网络在ColumbiaGaze数据集上的误差为7.2°,两模型的精度分别比iTracker和UlinFT提高了6.8%和2.7%。所构建的眼球驱动电动病床系统有效地提高了患者的生活水平,满足了患者的诸多需求。

The free-head 3 D gaze tracking is of great significance.The traditional eye tracking methods have problems of low accuracy,complex equipment and limited wearing.To address these issues,a monocular head free 3 D eye tracking technology based on machine learning is proposed.Two lightweight,high-precision and real-time eye tracking models are formulated,which can estimate the gaze point and gaze direction,respectively.For the gaze point estimation model,Dlib is used to locate the facial feature points to get the eye image.Then,PNP is used to get the head pose.Two kinds of information and part of the feature point coordinates are taken as the input into the multi-channel convolutional neural network.Finally,the gaze point is estimated.The gaze direction estimation network is a simplified version of gaze point estimation network.The proposed eye tracking technology is combined with the electric sickbed to establish a set of electric sickbed system based on eyeball drive,which allows patients to use their eyes to control the operation of the electric sickbed.Experimental results show that the error of the proposed gaze point estimate model on MPIIGaze dataset is 4.1 cm.The error of gaze direction estimation network in ColumbiaGazedata set is 7.2°,and the accuracy of the two models is 6.8%and 2.7%higher than those of iTracker and UlinFT,respectively.The system of eye driven electric sickbed can improve the living standard of patients and meet the needs of patients.