DRIVER DROWSINESS DETECTION BY USING WEBCAM

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

Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.Comment: 26 pages, 13 figures, 4 table

A study on tiredness assessment by using eye blink detection

In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy

Efficient and Robust Driver Fatigue Detection Framework Based on the Visual Analysis of Eye States

Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable due to the face covering with the mask. In this paper, we propose a robust visual-based fatigue detection system for monitoring drivers, which is robust regarding the coverings of masks, changing illumination and head movement of drivers. Our system has three main modules: face key point alignment, fatigue feature extraction and fatigue measurement based on fused features. The innovative core techniques are described as follows: (1) a robust key point alignment algorithm by fusing global face information and regional eye information, (2) dynamic threshold methods to extract fatigue characteristics and (3) a stable fatigue measurement based on fusing percentage of eyelid closure (PERCLOS) and proportion of long closure duration blink (PLCDB). The excellent performance of our proposed algorithm and methods are verified in experiments. The experimental results show that our key point alignment algorithm is robust to different scenes, and the performance of our proposed fatigue measurement is more reliable due to the fusion of PERCLOS and PLCDB

Driver monitoring system based on eye tracking

Dissertação de mestrado integrado em Engenharia Electrónica Industrial e ComputadoresRecent statistics indicate that driver drowsiness is one of the major causes of road accidents and deaths behind the wheel. This reveals the need of reliable systems capable of predict when drivers are in this state and warn them in order to avoid crashes with other vehicles or stationary objects. Therefore, the purpose of this dissertation is to develop a driver’s monitoring system based on eye tracking that will be able to detect driver’s drowsiness level and actuate accordingly. The alert to the driver may vary from a message on the cluster to a vibration on the seat. The proposed algorithm to estimate driver’s state only requires one variable: eyelid opening. Through this variable the algorithm computes several eye parameters used to decide if the driver is drowsy or not, namely: PERCLOS, blink frequency and blink duration. Eyelid opening is obtained over a software and hardware platform called SmartEye Pro. This eye tracking system uses infrared cameras and computer vision software to gather eye’s state information. Additionally, since this dissertation is part of the project "INNOVATIVE CAR HMI", from Bosch and University of Minho partnership, the driver monitoring system will be integrated in the Bosch DSM (Driver Simulator Mockup).Estatísticas recentes indicam que a sonolência do condutor é uma das principais causas de acidentes e mortes nas estradas. Isto revela a necessidade de sistemas fiáveis capazes de prever quando um condutor está sonolento e avisá-lo, de modo a evitar colisões com outros veículos ou objetos estacionários. Portanto, o propósito desta dissertação é desenvolver um sistema de monitorização do condutor baseado em eye tracking que será capaz de detetar o nível de sonolência do condutor e atuar em conformidade. O alerta para o condutor pode variar entre uma mensagem no painel de instrumentos ou uma vibração no assento. O algoritmo proposto para estimar o estado do condutor apenas requer a aquisição de uma variável: abertura da pálpebra. Através desta variável, o algoritmo computa alguns parâmetros utilizados para verificar se o condutor está sonolento ou não, nomeadamente: PERCLOS, frequência do pestanejar e duração do pestanejar. A abertura da pálpebra é obtida através de uma plataforma de hardware e software chamada SmartEye Pro. Esta plataforma de eye tracking utiliza câmaras infravermelho e software de visão por computador para obter informação sobre o estado dos olhos. Adicionalmente, uma vez que esta dissertação está inserida projeto: "INNOVATIVE CAR HMI", da parceria entre a Bosch e a Universidade do Minho, o sistema desenvolvido será futuramente integrado no Bosch DSM (Driver Simulator Mockup)

Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phone