Faster Single Model Vigilance Detection Based on Deep Learning

doi:10.1109/TCDS.2019.2963073

Faster Single Model Vigilance Detection Based on Deep Learning

2021-09-01

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DOI：10.1109/TCDS.2019.2963073

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Various reports have shown that the rate of road traffic accidents has increased due to reduced driver vigilance. Therefore, an accurate estimation of the driver's alertness status plays an important part. To estimate vigilance, we adopt a novel strategy that is a deep autoencoder with subnetwork nodes (DAE(SN)). The proposed network model is designed not only for sparse representation but also for dimension reduction. Some hidden layers are not calculated by randomly acquired, but by replacement technologies. Unlike the traditional electrooculogram (EOG) signals, the forehead EOG (EOG(F)) signals are collected through forehead electrodes that do not have to surround the eyes, which has a convenient and effective practical application. The root-mean-square error (RMSE) and correlation coefficient (COR) while separately using three EOG(F) features improved to 0.11/0.79, 0.10/0.83, and 0.11/0.80, respectively. Implemented in an experimental environment, percentage of eye closure over time is calculated in real time through SMI eye-tracking-glasses, up to 120 frames/s. In addition, the time to extract features from the raw signal and display the prediction is only 34 ms, that is the level of the driver's fatigue can be detected quickly. The experimental study shows that the proposed model for vigilance analysis has better robustness and learning capability.

Keywords:

Feature extraction

Electrooculography

Encoding

Training

Dimensionality reduction

Forehead

Computational modeling

Deep learning (DL)

dimension reduction

single model

vigilance detection

Faster Single Model Vigilance Detection Based on Deep Learning

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