期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 1, 页码 332-345出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2886012
关键词
Auto-encoder (AE); fuzzy Gaussian support vector machine (FGSVM); pupil detection; rotation; sphere Haar wavelets
类别
资金
- National Natural Science Foundation of China [61671293]
- Chinese Military Commission Equipment Development Department [61400030601]
- Open Project Program of the State Key Laboratory of CAD & CG, Zhejiang University [A1713]
This paper proposes a new method to solve the challenge of tracking pilot attention by integrating spherical Haar wavelet transform and deep learning methods. The method includes feature learning, spherical Haar wavelet, higher contractive autoencoder, and fuzzy Gaussian support vector machine components. The experimental results show the effectiveness of the model for spherical signal detection.
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.
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