Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot's Pupil Center Detection
Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot's Pupil Center Detection
复制标题
旋转球哈尔小波和带有模糊高斯支持向量机的深度收缩自动编码器网络用于飞行员瞳孔中心检测
DOI：
10.1109/tcyb.2018.2886012
复制
发表时间：
2021-01-01
影响因子：
11.8
通讯作者：
Sheng, Richard S. F.
中科院分区：
文献类型：
--
作者：
Wu, Edmond Q.;Zhou, Gui-Rong;Sheng, Richard S. F.
如何跟踪飞行员的注意力是一个巨大的挑战。我们能够捕捉到飞行员的瞳孔状态，分析他们的异常，并判断飞行员的注意力。本文提出了一种将球面Haar小波变换与深度学习方法相结合的方法来解决这一问题。首先，针对Haar小波和其他小波在球面信号分解与重构中的应用局限性，提出了一种基于球面Haar小波的特征学习方法。为了提取球面信号的显著特征，还提出了旋转球面Haar小波，它在重建图像和原始图像的同一方向上具有一致的尺度。其次，为了找到球面信号更好的特征表示，设计了一种高阶压缩自动编码器(HCAE)，用于球面Haar小波系数的势表示，它分别具有来自点x的Jacobian的两个惩罚项和来自点x的Taylor展开的两个阶项，用于样本空间的收缩学习。第三，为了提高分类性能，本文提出了一种模糊高斯支持向量机作为深度学习模型的顶层分类工具，它可以从深度HCAE网络(DHCAEN)的输出中剔除一些高斯噪声。最后，提出了一种DHCAEN-FGSVM分类器来识别瞳孔中心位置。在公共数据集和实际数据上的实验结果表明，该模型是一种有效的球面信号检测方法。
How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed. In order to obtain the salient features of the spherical signal, a rotating spherical Haar wavelet is also proposed, which has a consistent scale in the same direction between the reconstructed image and the original image. Second, in order to find a better characteristic representation of the spherical signal, a higher contractive autoencoder (HCAE) is designed for the potential representation of the spherical Haar wavelet coefficients, which has two penalty items, respectively, from Jacobian and two order items from Taylor expansion of the point x for the contract learning of sample space. Third, in order to improve the classification performance, this paper proposes a fuzzy Gaussian support vector machine (FGSVM) as the top classification tool of the deep learning model, which can punish some Gaussian noise from the output of the deep HCAE network (DHCAEN). Finally, a DHCAEN-FGSVM classifier is proposed to identify the location of the pupil center. The experimental results of the public data set and actual data show that our model is an effective method for spherical signal detection.