Study on Fuzzy Logic Decision Support System for Hypovigilance Detection Based on CNN Feature Extractor and WN Classifier

Fatigue and drowsiness are among the main causes of traffic accidents, just behind excessive speed and alcoholism. It is essential to monitoring continuously the driver’s vigilance level to ameliorate their ability to maintain safe and efficient driving. This paper deals with the problem of road safety. It attempts to present a driver vigilance monitoring system based on a video approach. This work aims at creating an assistive driving application employing eyes closure duration and head posture estimation as performant signs for alertness control. The proposed system can be summarized in three main steps: Eyes' detection and tracking in a video, eyes' state classification and fusion of both sub-systems based on eyes' blinking and head position. To accomplish the previous tasks, we used the Viola and Jones algorithm for interest area detection thanks to its efficiency in real time applications. For the classification step, we used two novel architectures of transfer learning classifier based on fast wavelet transform and separator wavelet networks, which presents our main contribution of this paper. This novel architecture proves its performance compared to the classic version of the transfer learning based on SVM classifier and to our old classifier based only on fast wavelet networks without a deep learning structure. The objective of our study is to test the efficiency of the CNN technique compared to wavelet networks in the classification phase. Also we aim to highlight the interest of fuzzy logic as a tool for merging different inputs, which allows us to have a more accurate system for vigilance control.