Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration

Abstract

Driver fatigue is one of the leading causes of road accidents. It affects the mental vigilance of the driver and reduces his personal capacity to drive a vehicle in full safety. These factors increase the risk of human errors which could involve deaths and wounds. Consequently, the development of an automatic system, which controls the driver fatigue and prevents him from accidents in advance, has received a growing interest. In this work, we have proposed a fusion system for drowsiness detection based on blinking measurement and the 3D head pose estimation. We have studied the driver's eye behaviors by analysing a non-stationary and non-linear signal and we estimate the head rotation in the three directions Y aw, Pitch, and Roll by exploiting only three interest points of the face. Our suggested system of fusion presents three levels of drowsiness: awake, tired, and very tired. This system is evaluated by both DE AP and Miracl H B databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.

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

“…Their systems do not take into account all the hypovigilance levels which are distraction, fatigue and sleeping. We found systems oriented only to fatigue levels' detection (tired, little tired, so tired) (Picot et al, 2012;Akrout and Mahdi, 2015) and others are interested in distraction detection (Céline et al, 2015). These examples of binary or general classification are founded in our previous work (Teyeb et al, 2014a;2014b;2015a;2015b) where two separated vigilance monitoring systems are based successively on eyes' blinking analysis and head pose.…”

“…Most of the previous works provided a vigilance classification into two (Liang and Lee, 2015), three (Picot et al, 2012) or four levels (Akrout and Mahdi, 2015). Their systems do not take into account all the hypovigilance levels which are distraction, fatigue and sleeping.…”

Weakly-Supervised Degree of Eye-Closeness Estimation

“…[2] just added a third level (partially opened) to the two eye states (opened and closed). [1] and [15] are drowsiness detection methods that uses the notion of percentage of eye openness (various states of eye openness). Both of them, to detect detailed eye states, use classical computer vision techniques.…”

“…Both of them, to detect detailed eye states, use classical computer vision techniques. [1] is a geometry shape-based approach which uses Circular Hough Transform method to localize iris and eyelids. Since it's a geometric-based approach, a very small variation in eyelids localization leads to a wrong decision, and it also easily gets affected by illumination variation.…”

“…To alleviate the data annotation burden we create a dataset of 1.3M synthetic data by rendering face images using computer graphics techniques. The dataset was created using high quality 3D scans of 13 human head models 6 female and 7 male from different age groups and ethnicities 1 . Eyeball models were separate, and placed in the 3D eye sockets.…”