Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines

Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines

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Aim:

          This paper aim to detect Driver Drowsiness Detection by using support Vector Machines.


Abstract:

      Driver drowsiness is a serious threat to road safety. Most driver monitoring systems (DMSs) already embedded in vehicles to detect drowsiness use vehicle-based features(i.e., measures) computed by outward-facing cameras for lane tracking or steering wheel angle sensors to analyze lane keeping and steering control behavior. Such DMSs are referred to as in direct DMSs as they monitor drowsiness indirectly through driving performance. In this work, we extend this classical technique by using a driver monitoring camera for tracking driver-based features associated with eye blinking behavior and head movements. We refer to DMSs based only on a driver monitoring camera as direct DMSs as they monitor drowsiness directly through observable driver-based behavioral cues. In this work, we conduct a comparative analysis between an indirect and direct DMS. We also combine vehicle-based and driver-based features to examine the potential of a so-called hybrid DMS. To this end, we use a database collected from 70 participants in driving simulator experiments. The comparative analysis is performed by means of the correlation-based feature selection technique and support vector machines independently.


Synopsis:

      Accidents are more due to the driver’s drowsiness; it has been recorded that more than 40% of chances that accidents occur while the driver’s is in drowsiness state. It’s very important that the driver must be in alert state while driving the car. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in Existing study, a low cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. Facial landmarks on the detected face are pointed and subsequently the eye aspect ratio and mouth opening ratio are computed and depending on their values, drowsiness is detected based on developed adaptive thresholding. In the proposed system, a webcam records the video and driver’s face is detected in each frame employing image processing techniques. A novel system for evaluating the driver’s level of fatigue based on face tracking and facial key point detection. In order to track the driver’s face using CNN (Convolution Neural Network) and then the facial regions of detection based on facial key points. Then the eyes and mouth will be detected if the eye is closed the alert system will be displayed.


Proposed System


           In modern days, we see how car accidents are increasing due to many reasons like drowsy driving or drunk driving or speeding and many more reasons. Hence we develop a modern solution, were my system will alert the driver if driver is sleeping. In the proposed system, a webcam records the video and driver’s face is detected in each frame for image processing techniques. A novel system for evaluating the driver’s level of fatigue based on face tracking and facial key point detection. In order to track the driver’s face using CNN (Convolution Neural Network) with Support Vector Machines and then the facial regions of detection based on facial key points. Then the eyes and mouth will be detected if the eye is closed the alert system will be displayed.


Advantage:


         Finally, our proposed method was compared with the state- of-the-art models on the EYE & MOUTH dataset, that the results of their model were the highest value in all recent works.


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