The present disclosure relates to a drowsiness detection system using a Raspberry Pi in which the system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required.
BACKGROUND OF INVENTION
[0002] In recent years driver fatigue is one of the major causes of vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver i.e. drowsiness. So it is very important to detect the drowsiness of the driver to save life and property. This project is aimed towards developing a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required.
[0003] Drowsiness detection has received an abundance of attention in recent years from both automotive companies and universities. A variety of approaches and methods have been researched and tested. These methods can be classified into three groups, namely, physiological methods, vehicle movement methods and computer vision methods.
[0004] Early methods made use of physiological measures such as respiration rate, heart rate and brain activity. These methods are, however, intrusive as measurement devices must be attached to the operator. This can be dangerous if the measurement devices restrict the movement of the operator. Furthermore, the devices often hinder the comfort of operators and cause distractions, increasing the risk of an incident or accident. Another method of drowsiness detection, geared towards drivers, is the monitoring of steering wheel movement and vehicle movement. Sensors are typically placed inside the steering wheel or dashboard of a vehicle and measure the

angular velocity and acceleration. This data is then used to classify erratic driving and swerving that is a characteristic of drowsy driving. A major drawback of this method is the large impact that road conditions and vehicle speed have on it. If road conditions are bad, drivers may swerve to avoid potholes and other obstacles which could be misinterpreted as drowsy driving. Additionally, the nature of the recorded data requires an extended period of analysis before an accurate classification of drowsiness can be made. This reduces the effectively of the system as an accident may occur before classification. Placement of the sensors may also be problematic as they may interfere with the driver if they are not built into the vehicle. [0005] Typically, the designed system deals with detecting the face area of the image captured from the video. The purpose of using the face area so it can narrow down to detect eyes and mouth within the face area. Once the face is found, the eyes and mouth are found by creating the eye for left and right eye detection and also mouth detection.
[0006] A computer vision based thoughts have been used for the creation of a Drowsy Driver Detection System. The little camera has been utilized by framework that concentrates straight towards the essence of driver and checks the driver's eyes with a particular ultimate objective to perceive weakness. A notice sign is issued to alert the driver, in such circumstance when exhaustion is perceived. The framework oversees using information picked up for the picture to find the facial tourist spots, which gets the area where the eyes of an individual may exist. On the off chance that the eyes of driver are discovered close for a specific measure of casings, the proposed framework accept that the driver is falling asleep and an alarm of caution has been issued. The structure can work just when the eye are found, and works in encompassing lighting conditions too.
[0007] Accordingly, the development of technologies for detecting or preventing drowsiness while driving is a major challenge in the field of

accident avoidance system. Because of the hazard that drowsiness presents on the road, methods need to be developed for counteracting its affects. The analysis of face images is a popular research area with applications such as face recognition, and human identification and tracking for security systems. This project is focused on the localization of the eyes and mouth, which involves looking at the entire image of the face, and determining the position of the eyes and mouth, by applying the existing methods in image-processing algorithm.
[0008] The invention is to develop a simulation of drowsiness detection system. The focus will be placed on designing a system that will accurately monitor the open or closed state of the driver's eyes and mouth. By monitoring the eyes, it is believed that the symptoms of driver's drowsiness can be detected in sufficiently early stage, to avoid a car accident. [0009] In the present subject matter is relevant to the implementation since fatigue and drowsiness drivers contribute to the percentage of road accidents. Many researches have been conducted to implement safe driving systems in order to reduce road accidents. Detecting the driver's alertness and drowsiness is an efficient way to prevent road accidents. With this system, drivers who are drowsy will be alerted by an alarm to regulate consciousness, attention and concentration of the drivers. This will help to reduce the number of road accidents.
[0010] US patent publication number 20170119298 relates method and apparatus of an eye gaze tracking system. In particular, the present invention relates to method and apparatus of an eye gaze tracking system using a generic camera under normal environment, featuring low cost and simple operation. The present invention also relates to method and apparatus of an accurate eye gaze tracking system that can tolerate large illumination changes. The present invention also presents a method and apparatus for detecting fatigue via the facial expressions of the user.

[0011] PCT application number 2020084469 relates to a machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value. [0012] AU patent publication number 2020102320 relates to an artificial intelligence based real time drowsiness detection system using machine learning. The proposed system continuously monitors the driver's facial expressions and detects their facial landmarks to extract their state of expressions. Once it detects such changes system take control of the vehicle and immediately slows down the vehicle and alerts the driver through an alarm. Herein the proposed system Raspberry Pi with an inbuilt camera for real-time frame capturing is adapted and a buzzer is also interfaced to the Raspberry Pi to generate an alarm. Following invention described in detail with the help of figure 1 of sheet 1 shows marking facial landmarks of the proposed system.
[0013] Chinese Patent number 106846734 discloses a kind of fatigue driving detection device and methods, acquire the head image of driver first, and processor device reads image and pre-processed. Then it by face characteristic classifier and improved mode locating human face region, when face is not detected, is constantly flashed by LED light and reminds

driver. Human eye is positioned in such a way that thickness combines. Ocular image binaryzation is found out the maximum inscribed circle in largest connected domain, opening and closing degree of the diameter of a circle as eyes by the color space for changing human eye area image. The frame number that eyes opening and closing degree is less than defined threshold in the statistical unit period accounts for the percentage of total frames, issues alarm when percentage is greater than 80% ; The fatigue strength for calculating driver, alarm is issued when fatigue strength is greater than the set value. Present invention employs human eye information collection modules, can reduce influence of the human body otherness to system accuracy, and the eye locating method of use is simple, can reduce the influence of glasses, and positioning accuracy is high, and real-time is good.
[0014] Thus, it is desired to address the above mentioned disadvantages or other shortcomings or at least provide a useful alternative.
OBJECTIVE OF INVENTION
[0015] The principal object of the embodiments herein is to capture image
continuously and measure the state of the eye according to the specified
algorithm and gives warning if required.
[0016] Another object of the embodiments herein is to detect the face area
of the image captured from the video.
[0017] Another object of the embodiments herein is to detect or prevent the
drowsiness while driving is a major challenge in the field of accident
avoidance systems.
[0018] Another object of the embodiments herein is to accurately monitor
the open or closed state of the driver's eyes and mouth.

[0019] Another object of the embodiments herein is to regulate consciousness, attention and concentration of the drivers who are drowsy will be alerted by an alarm.
[0020] Still, another object of the embodiments herein is to reduce the number of road accidents.
SUMMARYOF INVENTION
[0021] The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
[0022] Accordingly, there is provided a drowsiness detection system using a Raspberry Pi in which the system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required.
[0023] In accordance with an embodiment of a subject matter relates to a drowsiness detection system comprising Raspberry Pi configured with an inbuilt image sensor for real-time capturing a series of images of a face of operator. The image sensor is utilized by a framework that concentrates straight towards the essence of driver and checks the operator's eyes with a particular ultimate objective to perceive weakness. The framework oversees using information picked up for the images to find the facial tourist spots, which gets the area where the eyes of an individual may exist. Further, the drowsiness detection system includes a cascade object detector detecting the operator's face, nose, eyes, mouth or upper body, wherein the rectangle features can be computed rapidly using a representation for the at least one image of operator's face which is called integral image. In addition, the

system includes a facial landmark detection group which is configured to detect movement of a plurality of facial landmarks in the further series of images by using open CV. If the operator's face gets detected, the facial landmark detection group is applied and region of eyes is extracted such that once get the eye region, then calculate the eye aspect ratio to find out if the eye-lids are down for a substantial amount of time.
[0024] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the scope thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF FIGURES
[0025] In order to describe the manner in which features and other aspects of the present disclosure can be obtained, a more particular description of certain subject matter will be rendered by reference to specific embodiments which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments and are not therefore to be considered to be limiting in scope, nor drawn to scale for all embodiments, various embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:
[0026] FIGs. 1 and 2 illustrate flow diagram of a process for drowsiness detection system using Raspberry Pi, according to an embodiment as disclosed herein;

[0027] FIG. 3 illustrates graphical representation of techniques used in the
process of detecting face, eyes and mouth, according to an embodiment as
disclosed herein;
[0028] FIG. 4 illustrates circuit diagram of different components of
Raspberry Pi, according to an embodiment as disclosed herein;
[0029] FIG. 5 illustrates overview of survey of drowsiness detection system,
according to an embodiment as disclosed herein;
[0030] FIG. 6 illustrates graphical representation of parts of open CV for
drowsiness detection system, according to an embodiment as disclosed
herein;
[0031] FIG. 7 illustrates a block diagram of eye aspect ratio of the present
invention, according to an embodiment as disclosed herein;
[0032] FIGs. 8 and 9 illustrate a flow diagram of drowsiness detection
system using Raspberry Pi, according to an embodiment as disclosed herein.
DETAILED DESCRIPTION OF INVENTION
[0033] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term "or" as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the

examples should not be construed as limiting the scope of the embodiments herein.
[0034] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.
[0035] Accordingly, embodiments here are provided a drowsiness detection system using a Raspberry Pi in which the system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. [0036] Referring Figures 1 and 2, illustrating illustrate flow diagram of a process for drowsiness detection system using Raspberry Pi, according to an embodiment as disclosed herein. Methodology in research is defined as the systematic method to resolve a research problem through data gathering using various techniques, providing an interpretation of data gathered and drawing conclusions about the research data. This research onion model has six main layers, which serve as a step-by-step guide for researchers to create and organize their research methodology.
[0037] In accordance with an embodiment of the present subject matter relates to a method for detecting operator' s fatigue and drowsiness using Raspberry Pi comprising the steps of: providing Raspberry Pi with an inbuilt image sensor for real-time capturing a series of images of a face of operator. The image sensor is utilized by a framework that concentrates straight towards the essence of driver and checks the operator's eyes with a particular

ultimate objective to perceive weakness. Further, the method comprising the steps of detecting the operator's face, nose, eyes, mouth or upper body by a cascade object detector.
[0038] Referring figure 3 illustrating graphical representation of techniques used in the process of detecting face, eyes and mouth, according to an embodiment as disclosed herein. Few techniques have been used in the process of detecting face, eyes and mouth. In this technique used is cascade object detector. The cascade object detector uses the Viola-Jones algorithm process technique to detect people's face, nose, eyes, mouth or upper body. Rectangle features can be computed rapidly using a representation for the image which is called integral image. The value of the integral image at point (x, y) is the sum of all the pixels above and to the left. Based on the integral image, the sum of the pixels within rectangle D can be computed with four array references. The value of the integral image at location 1 is the sum of the pixels in rectangle A. The value at location 2 is A + B, at location 3 is A + C, and at location 4isA + B + C + D.
[0039] Referring Figure 4 illustrating circuit diagram of different components of Raspberry Pi, according to an embodiment as disclosed herein. The Raspberry Pi is the name of a series of single-board computers made by the Raspberry Pi Foundation, a UK charity that aims to educate people in computing and create easier access to computing education. The original Pi had a single-core 700MHz CPU and just 256MB RAM, and the latest model has a quad-core CPU clocking in at over 1.5 GHz, and 4GB RAM.
[0040] Referring figure 5 illustrating overview of survey of drowsiness detection system, according to an embodiment as disclosed herein. The outcomes of the study showed that the prototype is efficient, dependable and accurate as well in detecting the drowsiness of the driver. All this was done in real- time and represents non-intrusive fatigue monitoring. In another

study which they tried developing similar fatigue detection system, the proposed designed method to get the facial parts that include eyes and lips. [0041] Referring figure 6 illustrating graphical representation of parts of open CV for drowsiness detection system, according to an embodiment as disclosed herein. The Open CV machine vision left an Intel Research movement planned to drive CPU-raised applications. Toward this end, Intel pushed various endeavors that included continuous beam following and moreover 3D show dividers. Further, the drowsiness detection system comprises a facial landmark detection group configured to detect movement of a plurality of facial landmarks in the further series of images by using the open CV.
[0042] Referring figure 7 illustrating a block diagram of eye aspect ratio of the present invention, according to an embodiment as disclosed herein. To observe the aspect ratio of the eye remains constant for a period of time indicating that the eye was open, then it falls rapidly to zero and then increases again which indicates the operators blinked. Therefore, observing this eye aspect ratio in our drowsiness detector case to see if the value remains constant or falls to zero but not increases again implying that the driver has closed his eyes for extended period.
[0043] Referring figures 8 and 9 illustrating a flow diagram of drowsiness detection system using Raspberry Pi, according to an embodiment as disclosed herein. In this flow diagram is describing the steps by providing a camera which is setup at desirable position in a vehicle that looks for faces stream. If face gets detected, the facial landmark detection task is applied and region of eyes is extracted. Once get the eye region, then calculate the eye aspect ratio to find out if the eye-lids are down for a substantial amount of time.
[0044] In addition, starting to build the detector system with open CV: First, create a new file drowsy_detect.py and write the following script in it. Now,

for calculating the eye aspect ratio we need to compute the Euclidean distance between the facial landmarks points which in turn needs SciPy package in python. (It not a strict requirement but SciPy is needed if work related to computer vision or image processing is intended). Also the package named imutils is needed for image processing and computer vision functions to assist the working with OpenCv. For detecting and localising facial landmarks we will require the dlib library hence we import it. Eyeaspectratio function is defined to calculate the distance between the eye landmarks taken vertically and distances between the eye landmarks taken horizontally.

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