Abstract
Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.
Highlights
In this age of era, road accidents happens to be caused by lot of reasons where one of that reason happens to be driver feeling drowsy during driving known as fatigue driving
Driver facial features based detection methods are based on facial features using various methods, i.e. Convolutional Neural Networks (CNN) based Deep Learning Model [4],Generative Adversial Networks (GAN) [5],Principal Component Analysis [6], DriCare [7] where analysis was done on pupil, eyelids and head pose to detect drowsiness
This research proposes a robust method for vehicle driver drowsiness detection using facial features based head orientation and pupil detection where frame aggregation strategy is used to ensure facial features processing under challenging circumstances, i.e. light reflection and shadow
Summary
In this age of era, road accidents happens to be caused by lot of reasons where one of that reason happens to be driver feeling drowsy during driving known as fatigue driving. They showed how a Generative Adversarial networks (GAN) can be used to produce training data or individuals where model is failing by generating realistic images Their proposed approach did not rely on any meta-data or assumptions about the race or ethnicity of individuals in the datasets, which is a commonly used approach to determine algorithmic fairness or bias. A novel visualization technique which can be assistance to identify groups of people was proposed by research in [6] where potential discrimination could arise due to the usage of Principal Component Analysis (PCA) They used PCA to produce a grid of faces sorted by similarity and combining these with a model accuracy overlay. They did not compare effectiveness of their proposed DriCare with other existing methods
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