DRIVER DROWSINESS DETECTION USING MACHINE LEARNING: A REVIEW

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DRIVER DROWSINESS DETECTION USING MACHINE LEARNING: A REVIEW

R Tanseer Ahmed

_{, Mehtab Mehdi}

*1

_{Department of Computer Science and IT, Jain (Deemed to be University), Bangalore,}

Karnataka, India.

*2

_{Professor, Department of Computer Science and IT, Jain (Deemed to be University), Bangalore,}

Karnataka, India.

ABSTRACT

Consistently numerous individuals lose their lives because of deadly street accidents; driver drowsiness is one of the major reasons for traffic accidents around the world. 1 in 4 vehicle mishaps are brought about by drowsy driving. drowsy driving simply nodding off while driving. Drowsy driving can be as little as a concise condition of obviousness when the driver isn't giving full consideration to the road. To forestall such mishaps, it is important to identify driver laziness as early as possible. The traditional method to recognize tiredness depend behavioural aspects. A comparative analysis is done using machine learning algorithms with Logistic Regression, Naïve Bayes, K- Nearest Neighbor and Random Forest.

Keywords: Driver drowsiness, Machine learning, Fatigue detection, Behavioral measures, Classification Models.

I.

INTRODUCTION

Road accidents are one of the most affected casualties for people, drowsiness driving contributes 40% of road accidents. After continuous driving for long time, driver feels exhausted and fatigue which eventually leads to drowsiness. Statistics related to drowsy driving vary amongst different countries. Creating innovation for recognizing driver weariness to decrease mishap is the fundamental test. As indicated by the report by "Service of Road Transport and Highways" there were 4,552 mishaps announced each year in India that took lives of thousands of people on account of sluggish drivers (Road Accidents in India 2016) Many heavy transportation vehicles travel during night, drivers of such vehicles who drive for continuous long period become susceptible to these kinds of situations. Drowsiness detection of drivers is a progressing research in order to minimize such tragic incidents. Many systems are intended to examine driver weariness and to identify driver tiredness. which can be made as integral part of future intelligent vehicles so as to prevent sleep related road accidents. Typical methods used to identify drowsy drivers are physiological based, vehicle based, and behavioural based Psychological methods such as heartbeat, pulse rate and Electrocardiogram. Vehicle based methods include accelerator pattern, acceleration and steering movements. Behavioural methods In this paper, a scheme is proposed based on extraction of visual features from the data without human intervention. These visual features have been learnt using a model of machine learning classifiers. The feature maps the learnt weights with input image act as the features for driver drowsiness detection. Using these set of features a soft-max layer classifier is utilized to finally classify the frames extracted as drowsy or non-drowsy. A comparative analysis amongst ML (Machine Learning) algorithms is done to get maximum efficiency and accuracy as to which one predicts the better outcome. Further, a set of extra methodologies are suggested that could be combined with the scheme in the future to make the technique more robust.

II.

RELATED WORK

There are many researches done with drowsiness detection the aim is to prevent such miss happening Typical way of detecting drowsiness is by having a camera which detects the motion and level of drowsiness Some technologies use sensors to detect movements and Eye closure ratio. The System gives an alert to the driver when the eye closure goes below threshold value. Using machine learning prediction are done so as to which classifier gives the most precise and accurate results This section is an outline of existing approaches to identify driver drowsiness. Ashish et al (A. Kumar, R. Patra 2018) detected real time driver laziness dependent on visual behaviour, mouth aspect ratio as a parameter to detect drowsiness. Using SVM and FLDA algorithms. Sukrit et al (S. Mehta, S Dadhich, S Gumber, AJ Bhatt, 2019) used Eye perspective proportion and Eye closure as parameter for detection with and accuracy of 84% using random forest. Mohsen et al (M Babaeian, N Bhardwaj, B Esquivel, M Mozumdar) Heart rate variation was used to detect drowsiness, Logistic regression provided 90% Maintaining the Integrity of the Specifications accuracy in the model. Kartik et al (K Dwivedi, K Biswaranjan, A Sethi) deep learning and multi-layer CNN algorithm was used for detecting drowsiness using features like eye blinks, eye closure, forehead strain marks or even eyebrow shapes accuracy achieve was 92.33%. Rateb et al. (R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang,2018)

detected real-time driver drowsiness using deep neural networks, developed an Android application. Thus, with reference to the literature work a comparative study is proposed that detects driver’s drowsiness.

III.

METHODOLOGY

In the proposed approach to distinguish driver's laziness various machine learning algorithms has been used for comparison as to which one gives more accurate results. Facial landmarks EAR (Eye Aspect Ratio), Mouth opening ratio (MOR) are used to compute. If the EAR and MOR esteem is not exactly the threshold value, then this would indicate a state of fatigue. In case of Drowsiness, the driver and the passengers would be alerted by an alarm. Using classification model’s prediction is done based on training and testing dataset. If the driver blinks his eyes it shouldn’t be categorized as drowsy to which the EAR has a threshold value and the minimum timebound.

Facial Landmarking

To extract facial landmarks of drivers, Dlib library was imported and deployed. The library utilizes a pre-prepared face detector, which depends on an alteration to the histogram of situated inclinations and uses linear SVM (support vector machine) technique for object recognition. Face landmark were initialized and captured which were used for calculating distance between the points. EAR stand for Ear Aspect Ratio where numerator denotes height of eye and denominator denotes the width of eye, the numerator calculates the distance between the upper eyelid and the lower eyelid. The denominator represents the horizontal distance of the eye. When the eyes are open, the numerator value increases, thus increasing the EAR value, and when the eyes are closed the numerator value decreases, thus decreasing the EAR value. details of eye and its landmark is portrayed in Figure 1, Figure 2 represents a snapshot of facial landmark points using Dlib library, which are used to compute EAR.

EAR = (|𝑝2 – 𝑝6| + |𝑝3 – 𝑝5|) 2∗|𝑝1-𝑝4|

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Figure 2:

Facial Landmark Point according Dlib Library

IV.

CLASSIFICATION

After extraction and normalization of features the model is tested under classification modelling technique ranging from basic model such as Logistic regression, Naive Bayes moving on to complex model as K-NN, Random Forest

.

Logistic Regression

Logistic Regression is used when the dependent variable(target) is categorical. Logistic regression is a predictive analysis. It is utilized to portray information and to clarify the connection between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. To plan anticipated qualities to probabilities, we use, we use the sigmoid function. The function maps any real value into another value between 0 and 1 In a binary logistic regression model, the dependent variable has two levels(categorical). Yields with multiple qualities are demonstrated by multinomial logistic regression. and, if the multiple categories are ordered, by ordinal logistic regression.

Naive Bayes

Naive Bayes is a classification technique that assumes the presence of a specific component in a class irrelevant to the presence of some other element. Naive Bayes model is anything but difficult to fabricate and especially helpful for very large data sets. It's anything but a solitary calculation yet a group of calculations where every one of them share a typical rule, for example each pair of highlights being grouped is free of one another Naïve Bayes is also known to beat even exceptionally complex classification models.

K-NN

K-nearest neighbors (KNN) algorithm is a supervised Machine Learning algorithm which can be used for both classification and regression for predicting problems but mostly it is used for the Classification problems. K-NN calculation expects the likeness between the new case/information and accessible cases and put the new case into the class that is generally like the accessible classifications. It is also called a lazy learner algorithm because it doesn't gain from the preparation set promptly rather it stores the dataset and at the hour of grouping, it plays out an activity on the dataset, at the preparation stage just stores the dataset and when it gets new information, at that point it characterizes that information into a classification that is much similar to the new data.

Random Forest

Random forest is a supervised learning algorithm. The "forest" it which, is gathering of decision trees, usually trained with the “bagging” method. he overall thought of the sacking technique is that a mix of learning models expands the general outcome. It fabricates different choice trees and combines them to get more precise and stable forecast. Random Forest models combine the simplicity of Decision Trees with the adaptability and intensity of a gathering model

V.

RESULTS

The results which are obtained has discrete qualities going from one classification model to another. Dataset which is used stays common amongst all the classification model. The aim is to find out which classifier performs most accurate results from the table 1 it shows K-NN reported the highest accuracy at 77.21% and Naïve Bayes performed the worst at 57.75%.

.

SN. Classifier Accuracy 1 Logistic Regression 64.33% 2 Naïve Bayes 57.75% 3 K- Nearest Neighbor (K=25) 77.21%

4 Random Forest (max

depth = 8)

70.50%

VI.

CONCLUSION

This paper proposes a comparative analysis amongst machine learning classifiers. The face of driver has been detected by capturing facial landmarks and alerts is given to the driver hence avoiding real time crashes. Non-intrusive methods have been preferred over nosy techniques to prevent the driver from being distracted due to the sensors attached on his body. The proposed approach uses nosy techniques in real-time. This is useful when drivers are in long journey and drive continuously. The classifier predicts on training and test dataset. The accuracy achieved by these classification models are put into comparison so as to know which one preforms better and which performs least.Future work can be done with different classification models which may give much accurate values by taking larger datasets. This can further be carried out with more feature extraction which can classify better. The system can be implemented with people with spectacles. The work can be extended with biker’s who travel for longer durations this can also be implemented in working environments so as to keep the employee alert.

VII.

REFERENCES

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