International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 06 | Jun 2022
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
REAL-TIME DRIVER DROWSINESS DETECTION Dhivya.M1 M.E, Harish V2, Inbasakaran S3, Aswin Charles L4 1 Assistance
Professor, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu, India 2-4 Student, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------fitted to the boundary between the top region and also the Abstract - Deep learning techniques have been used inorder
neural network), Drowsiness Detection, Deep Learning.
background and also the face are going to be detected. The human coloring and texture faces have also proven to be good features for face detection. For this method, the foremost important feature was the color which will be separated from other parts of the background. This method used maximal varieties variance threshold. Another method used for face detection was the histogram intersection within the HSV color space to spotlight the skin region. The template matching methods store several patterns of various faces to explain as a full or the face expression separately, by computing the correlations between an input image and also the stored pattern so as to work out the degree of similarity of the pattern to a face. There are several techniques which are used with this method. For detecting the features from the face here we use the CNN algorithm which extracts the features from the image screens. By detecting the features of eyes from the image, whether it's closed or opened we are able to identify the Drowsiness level of the motive force.
1. INTRODUCTION
2. LITERATURE REVIEW
One of the foremost frequent causes of road accidents is said to driver’s drowsiness. The statistics show that drowsiness expose driver to higher crash risks, severe physical injuries, or maybe death, while the economic losses aren't negligible. A drowsy driver is in an exceedingly state of mental and physical flabbiness, which has decreased mental alertness and a sensation of tiredness. Being during this state, he not performs competence to safety driving man oeuvre. Drowsy driving could be a real problem in our society because it affects and puts at risk all traffic participants - drivers and pedestrians. the event of a system, which monitors, in real time, the driver’s level of drowsiness will decrease the amount of car accidents and can save immeasurable lives everywhere the globe. the employment of such an assisting system, ready to measure the amount of vigilance is critical in car crash prevention. so as to develop the system is vital to understand to judge the extent of drowsiness. Four kinds of measurements are commonly accustomed check the extent of drowsiness. There are several approaches employed in face detection. a number of these encode the knowledge about characteristics of typical face and find structural elements - like eyebrows, eyes, nose, mouth and hairlineand use the relationships between them to detect faces. in a very method to spot the face from a cluttered background supported segmentation was proposed. The eclipse was
The developed system may be a real time system. HAAR based cascade classifier is employed for face detection. An algorithm to trace objects are wont to track the eyes continuously. so as to spot the drowsy state of the driving force, the CNN algorithm is employed. The paper focuses on developing a non-interfering system which might detect fatigue and issue a warning on time. The system will monitor the driver’s face employing a camera. By developing an algorithm, the drowsy symptoms of driver are detected early enough to avoid accident. When the signs of fatigue are identified then a sound are produced as an attentive to the driving force unless he's awake from fatigue. Alert sound are deactivated automatically when the driving force is awake from the fatigue for a period of your time. this method will detect driver’s fatigue by the processing of the attention region. After image acquisition, the primary stage of processing is face detection. If the eyes are closed for quite 0.7 seconds, this technique issues alert sound to the motive force. System makes use of the amount of eye blinks for detecting the state of drowsiness in an exceedingly driver. If eye of the driver is blinking it will not consider that as an issue. The system makes use of OpenCV and a camera. the attention status is obtained through image processing algorithms. this method takes under consideration only the state of the eyes, it doesn't concentrate on the frequency of
to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificial intelligence. An intelligence System has been developed to detect the drowsiness of the driver which can prevent accident and reduces loss and sufferings. A driver’s condition can be estimated by bio-indicators, behaviour while driving as well as the expressions on the face of a driver. In this paper we present an all-inclusive survey of recent works related to driver drowsiness detection and alert system. We also present the various deep learning techniques such as CNN which specially designed to work with images and videos, HAAR based cascade classifier, OpenCV which are used in order to determine the driver’s condition. Finally, we identify the challenges faced by the current system and it can be enhanced in future to the vehicles
Key Words: Artificial Intelligence, CNN (Convolutional
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