Matlab code for Drowsy Driver Detection
Although the DCFG successfully grabs the video signal and digitizes it, the limitation to this design was that the parallel port could not transfer the signal fast enough for real-time purposes. Thanks to Noelia Hernandez and Ivan García for helping me developing this system and all the people who let us record them while they were driving. The valleys dips in the plot of the horizontal values indicate intensity changes.
John is a nice, outgoing guy, who carries a smart, witty demeanor. Highly reflective object behind the driver, can be picked up by the camera, and be consequently mistaken as the eyes. Any suggestion implementing on that? This project is focused on the localization of the eyes, which involves looking at the entire image of the face, and determining the position of the eyes by a self developed image-processing algorithm.
Matlab code for Drowsy Driver Detection - A large distance corresponds to eye closure. This is shown in Figure 7.
Description: In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. So there is a need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. There are two methods for drowsiness detection. The first one is intrusive methods and the second one is non-intrusive methods. The intrusive methods include measurement of heartbeat rate, mind wave monitoring etc. In this paper, various methods are included by which the drowsiness can be detected and warning can be issued to the driver while driving. And also compared different parameters for different methods. In recent years, driver drowsines s has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significan t economic losses. So there is a need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. There are two methods for drowsiness detection. The first one is intrusive methods and the second one is non-intrusive methods. The intrusive methods include measurement of heartbeat rate, mind wave monitoring etc. It is most accurate, but it is not realistic, because sensing electrodes would have Drowsiness is a process in which one level of awareness is reduced due to lacking of sleep or fatigue and it may cause the driver fall into sleep quietly. When the driver is suffering from drowsiness driver loses the control of the car, so driver might be suddenly deviated from the road and hit an obstacle or a car to overturn. According to available statistical data, over 1. Based on police reports, the US National Highway Traffic Safety Administration NHTSA conservatively estimated that a total of 100,000 vehicle crashes each year are the direct result of driver drowsiness. In the year 2009, the US National Sleep Foundation NSF reported that 54% of adult drivers have driven a vehicle while feeling drowsy and 28% of them actually fell asleep. The German Road Safety Council DVR claims that one in four highway traffic fatalities are a result of momentary driver drowsiness. These statistics suggest that driver drowsiness is one of the main causes of road accidents. These statistics in the UK are 16 to 20 percent of accidents reported by police. Legal Medicine Organization of Iran's research shows that most road accidents statistics in the world happen in Iran. The financial costs inflicted in Iran road due to road accidents are estimated about 4000 million dollars more than 3. According to Iran's Police department in 2006 and 2007, 23% of accidents are cause This method includes deviations from lane position, movement of the steering wheel, pressure on the acceleration pedal, etc. In most cases, these measurements are determined in a simulated environment by placing sensors on various vehicle components, including the steering wheel and the acceleration pedal; the signals sent by the sensors are then analysed to determine the level of drowsiness. A drowsy person displays a number of characteristic facial movements, including rapid and constant blinking, nodding or swinging their head, and frequent yawning. Computerized, non-intrusive, behavioural approaches are widely used for determining the drowsiness level of drivers by measuring their abnormal behaviours. Most of the published studies on using behavioural approaches to determine drowsiness, focus on blinking. PERCLOS which is the percentage of eyelid closure over the pupil over time, blinks has been analysed in many studies. This measurement has been found to be a reliable measure to predict drowsiness and has been used in commercial products such as Seeing Machines and Lexus. Some researchers used multiple facial actions, including inner brow rise, outer brow rise, lip stretch, jaw drop and eye blink, to detect drowsiness. However, research on using other behavioural measures, such as yawning and head or eye position orientation, to determine the level of drowsiness. In addition, long time usage would result in perspiration on the sensors, diminishing their ability to sens e accurately. The correlation between physiological signals electrocardiogram ECG , electromyogram EMG , electro Oculogram EoG and electroencephalogram EEG and driver drowsiness has been studied by many researchers. As drivers become drowsy, their head begins to sway and the vehicle may wander away from the centre of the lane. The previously described vehicle-based and vision based measures become apparent only after the driver starts to sleep, which is often too late to prevent an accident. However, physiological signals start to change in earlier stages of drowsiness. Hence, physiological signals are more suitable to detect drowsiness with few false positives; making it possible to alert a drowsy driver in a timely manner and thereby prevent many road accidents. Face detection, face component detection, Infrared sources, Vehicle Accident Warning, Advanced Vehicle Safety, Intelligent Vehicles, Driver Fatigue Warning, Eye Tracking System, Driver Assistance mind wave, EEG, embedded system, real-time ECG, EMG Physiological measures, Driver inattention, Driver distraction Sobel edge detection, Erosion Background sub. The comparison between different implementation methodologies for detecting driver drowsiness is as shown in table 1. Different parameters are taken for comparison such as Area of implementations, implementation methodologies, Resources used and Algorithm of implementation. The advantage of this method is, it is highly accurate, but it has disadvantages like it is an intrusive method. Using the sensors for longer period, results the false data due to perspiration, so it is non-realistic method. The other intrusive method is brain wave monitoring method. In this method the alpha and beta wave are monitored using sensors on head. It was developed on a dedicated embedded system. The waves between 12 to 30 Hz are beta waves. They are associated with concentration. The waves between 8 to 12 Hz are alpha waves. They are associated with relaxation and state of mental calm. The resources used in this method was readily available, so that makes the implementation easier and faster. The accuracy of brain wave monitoring method is also high, but it is also affected by the surrounding signal noise and it is not comfort able for driver to wear the device on head while driving. So it makes it non-realistic to use. Eye blinking rate remains moderate in normal state o f driver, but increases or decreases when driver is feeling drowsy. By this the result can also be achieved in darker environment, especially at night time. This provides additional warning, rather than only providing sound warning. This setup provides easiness of implementation, as the MATLAB contains most of the codes readily available in it, only algorithm is needed to be developed. There are also several disadvantages of MATLAB. It is Costly and bulky software. In terms of performance and response ti me, result of MATLAB is also poor. The setup with Laptop becomes larger in size. The working of system totally depends on battery of laptop. So it becomes unreliable to use this setup in real working condition. Different algorithms are used for image processing techniques. The main advantage of this method is, it is non-intrusive method, so it does not distract driver while operation, as well it does not introduce any discomfort to driver during operation. It works on Laptop, which also make it unreliable for use in real environment. Some solutions for this problem are,.
How can I get the source code for the same? This report describes how to find the eyes, and also how to determine if the eyes are open or closed. Assuming that the driver s head is not at an angle tilted , the y coordinates of the left and right eye should be approximately the same. The aim of this project is to develop a prototype drowsiness detection system. When the feature determines if the driver is fatigued, the message center displays the warning, TAKE A BREAK! This is due to the blobs of black pixels on the face, primarily in the eye area, as seen in Figure 7. Whoever spread it was desperate to signal that a legitimate regulated entity was getting involved in the dinar trade, cuts, or Mission 3 Signed in Blood, its time to come back down to Earth. The greyscale image is converting to a binary image via thresholding. If it is, we increment COUNTER , the total number of consecutive frames where the person has had their eyes closed. This may also eliminate the need for the noise removal function, cutting down the computations needed to find the eyes. In most cases, these measurements are determined in a simulated environment by placing sensors on various vehicle components, including the steering wheel and the acceleration pedal; the signals sent by the sensors are then analysed to determine the level of drowsiness. Parts of the background and surrounding objects are in shadow, and can also affect the brightness values in different regions of the object.