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Project on Detection of Drowsiness in Drivers
Project on Detection of Drowsiness in Drivers

Project on Detection of Drowsiness in Drivers

Sleepy drivers are one of the causes of road accidents, which claim many fatalities each year. Because drowsiness is a possible cause of road danger, one of the best methods to avoid it is to install a drowsiness detection system. Another technology that can save many lives is a driver sleepiness detection system that continuously assesses the driver’s eyes and alerts him with alarms if the system detects that the driver closes his eyes very often. A webcam is required for this project for the system to monitor the driver’s eyes regularly. This Python project will require a deep learning model as well as packages such as OpenCV, TensorFlow, Pygame, and Keras to do this.

Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking effort to get enough sleep before driving, drink coffee or energy drink, or have a rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG. This method has high accuracy for its measurement but it need to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor [18]. Thus, it is not comfortable to be used in real time driving. This paper proposes a way to detect the drowsiness signs among drivers by measuring the eye closing rate and yawning. This project describes on how to detect the eyes and mouth in a video recorded from the experiment conducted by MIROS (Malaysian Institute of Road Safety). In the video, a participant will drive the driving simulation system and a webcam will be place in front of the driving simulator. The video will be recorded using the webcam to see the transition from awake to fatigue and finally, drowsy. 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. The parameters of the eyes and mouth detection are created within the face image. The video were change into images frames per second. From there, locating the eyes and mouth can be performed. Once the eyes are located, measuring the intensity changes in the eye area determine the eyes are open or closed. If the eyes are found closed for 4 consecutive frames, it is confirm that the driver is in drowsiness condition.

Drowsiness is a state of near sleep, where the person has a strong desire for sleep. It has two distinct meanings, referring both to the usual state preceding falling asleep and the chronic condition referring to being in that state independent of a daily rhythm [16]. Sleepiness can be dangerous when performing tasks that require constant concentration, such as driving a vehicle. When a person is sufficiently fatigue while driving, they will experience drowsiness and this leads to increase the factor of road accident. Figure 1: Statistic of Road Accident from 2005 to 2009 Figure 1 shows the statistic of road accident in Malaysia from the year 2005 to 2009 provided by MIROS (Malaysia Institute of Road Safety). The numbers of vehicles involved in road accident keep increasing each year. From Figure 1, car and taxi type of vehicles shows about nearly 400,000 cases of road accident has been recorded. It keeps increasing every year and by the year 2009, it shows the number of road accident were recorded by MIROS are nearly 500,000.  Figure 2 shows the difference between fatigue and drowsiness condition. Examples of Fatigue & Drowsiness Condition 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 aim of this project 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. Yawning detection is a method to assess the driver’s fatigue. When a person is fatigue, they keep yawning to ensure that there is enough oxygen for the brain consumption before going to drowsiness state . Detection of fatigue and drowsiness involves a sequence of images of a face, and the observation of eyes and mouth open or closed duration. Another method to detect eye closure is PERCLOS. This detection method is based on the time of eyes closed which refers to percentage of a specific time. 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 imageprocessing algorithm. Once the position of the eyes is located, the system is designed to determine whether the eyes and mouth are opened or closed, and detect fatigue and drowsiness.

Driver drowsiness and fatigue are significant causes of road accidents. Every year, they increase the number of deaths and fatalities worldwide. A module for an advanced driver assistance system is presented in this system to reduce the number of accidents caused by driver fatigue and thus increase transportation safety; this system deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence.

The proposed OpenCV algorithms effectively find and help to normalize human faces while causing the majority of accidents related to vehicle crashes. The algorithm begins by detecting heads on color images using color and structure deviations in the human face and background. Several faces and body gestures, including tiredness in the eyes and yawning, are regarded as signs of drowsiness and fatigue in drivers. These characteristics indicate that the driver’s condition is poor.

One of the most common causes of accidents is driver drowsiness and fatigue. Each year, the number of people killed in such accidents rises around the world. In Driver Drowsiness Detection System, to log in to the system the admin can log in with a username and password. The admin can view the list of all the users and also can view their logs.

 

The user has to register their account and log in using a username and password. Using Open CV, the system will detect eye closure or yawning actions in real-time. If it finds any, it will draw a red rectangle and add a log to the table. The user can view their logs with details in My Logs.

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