Abstract
Fatigue and drowsiness are among the main causes of traffic accidents, just behind excessive speed and alcoholism. 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. Different datasets with different classifiers are used to evaluate our new approach. Our second contribution is illustrated by the final system which uses the fuzzy logic and provides five different vigilance levels. Global rates given by experimental results show the effectiveness of our proposed classification system for eyes' state recognition and driver drowsiness detection.
Highlights
Driving is a complex activity that involves many tasks: Finding the way, following the road, monitoring the speed, avoiding obstacles, respecting the rules of the road and controlling the vehicle, etc 14
We compared three different eyes classifiers based on Wavelet Network (WNC) learnt by the Fast Wavelet Network (FWT), the classic architecture of transfer learning based on Alexnet model and Support Vector Machine (SVM) (ASVM) and our first proposed classifier based on alexnet Architecture and the Fast Wavelet Network (AFWT) and our second proposed classifier based on Alexnet and the Separator Wavelet Network (ASWNC)
We aim to show the efficiency of our system based on the two proposed techniques which are (Alexnet + Wavelet Network Classifier (WNC) and alexent + Separator Wavelet Network Classifier (SWNC)) compared to the standard architecture of alexnet based on the SVM classifier
Summary
Driving is a complex activity that involves many tasks: Finding the way, following the road, monitoring the speed, avoiding obstacles, respecting the rules of the road and controlling the vehicle, etc 14. It is essential to monitoring continuously the driver’s vigilance level to ameliorate their ability to maintain safe and efficient driving. This lack of vigilance may take many forms such as, drowsiness, fatigue and distraction. We propose a novel approach for driving assistance based on a multimodal system by fusing our cited systems. This new application allows us to detect five levels (alertness, distraction, fatigue, micro-sleep and full sleep) which are different to those cited in the literature
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