Driver fatigue detection based on eye tracking and dynamic template matching

[[abstract]]A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging and promising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.[[conferencetype]]國際[[conferencedate]]20040321~20040323[[conferencelocation]]臺北市, 臺

A Low Cost Real Time Embedded Control System Design Using Infrared Signal Processing with Application to Vehicle Accident Prevention

Vehicle accidents are most common if the driving is inadequate. These happen on most factors if the driver is drowsy or if heis alcoholic. Driver drowsiness is recognized as an important factor in the vehicle accidents. It was demonstrated that drivingperformance deteriorates with increased drowsiness with resulting crashes constituting more than 20% of all vehicleaccidents. But the life lost once cannot be re-winded. Advanced technology offers some hope avoid these up to some extent.A car simulator study was designed to collect physiological data for validation of this technology. Methodology for analysisof physiological data, independent assessment of driver drowsiness and development of drowsiness detection algorithm bymeans of sequential fitting and selection of regression models is presented. In this paper proposes an approach towardsdesign of a Low cost real time embedded control system which involves measure and controls the eye blink using sensor. Ascar manufacturers / industrial automotive communities, incorporate intelligent vehicle systems in order to satisfy theconsumer’s ever increasing demand for more assistant systems for comfort, navigation, or communication, to address theissue of increased level of cognitive stress on drivers to the sources of distraction from the most basic task at hand, i.e.,driving the vehicle. Driver’s drowsiness detection systems are actually receiving a large interest in the academic andindustrial automotive communities for their potentiality to reduce fatalities Eye detection is a crucial aspect in many usefulapplications ranging from face recognition / detection to human computer interface for, driver behavior analysis. Visionbaseddriver fatigue detection which is non-contact has a key advantage over applicability. In this paper proposes a simpleand economical prototype design as a solution in developing a intelligent vehicles based on IR signal processing formonitoring the driver’s drowsiness level, vigilance and alerting the driver to prevent accidents. This approach is economicaland all the lower income side vehicle owners can afford to installation of this system.Keywords- Intelligent Vehicles, Driver Vigilance, Human fatigue, Safe Navigatio

Computer Based System for Sleep Detection of Driver

Driver drowsiness represents an important risk on the roads. It is one of the main factors leading to accidents or near-missed accidents. So there is a need to develop a system to detect drowsiness of a driver so that the risk of accidents due to drowsiness can be reduced. The Computer Based System to Detect Sleep of a Driver includes the process for detecting the drowsiness of a driver. It detects driver drowsiness and progressively warns the driver, so that he/she can either correct the behavior or stop driving .Video of the driver is taken as input to the system from this video frames are extracted. Then each extracted frame is processed by the system. First of all, blurring of image is done. Then RGB to HSV conversion of image is done. After conversion of image to HSV color format thresholding is applied. Head movement of driver is tracked through camera and offset is set. If head movement offset is greater than set offset value drowsiness of driver is detected and alarm is generated. DOI: 10.17762/ijritcc2321-8169.15030

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

VLSI Implementation of Eye Detection System

The eye detection is one of the most challenging problems in many applications such as image processing, pattern recognition and computer vision. This paper introduces efficient vlsi implementation of eye detection system. Face detection is a very important part of the developed eye detection algorithm. Face detection is done by using skin detection method. Skin detection is an extraction of skin color pixels and regions from an image. In this method, first input image is converted from RGB image to YUV image. With YUV domain, the skin pixels in an image are extracted. Then Morphological operation is done by using erosion method through which noise of an image is also eliminated. From this image, face region is extracted by identifying the skin pixels. Then eye detection is done according to rules of human face proportion. The eye detection system is implemented using Verilog-HDL and simulation is done by using Xilinx ISE

Enhancing protection of vehicle drivers and road safety by deploying ADAS and Facial Features Pattern Analysis (FFPA) technologies

The latest technology associated with Intelligent Transportation Systems (ITS) have been designed with the aim to minimize the numbers of person injury in road accidents and improve the overall road safety. The driver behavior is one major concern in many accidents in HK urban road links. In particular, the driver\u27s attitudes, such as fatigue, drowsiness and concentration are the major causes to road accidents. It will affect the driver\u27s ability and decisions in properly controlling their vehicles. Very often, this kind of driver distraction is particularly obvious when driving after 2 to 3 hours from most research sources. In the traffic data sourced from Transport Department of HKSAR, around 82% of the personal injury in road accidents belongs to the driver\u27s fault. This paper used the latest technology and applied it to a group of transport vehicles, i.e. taxi. The objective is set up to monitor, record and analyze the fatigue and drowsiness situation of drivers by means of advanced AI system, facial recognition detection system (the sensors) and early warning devices (LDWS) via ADAS technology. The result will be used to give real time early warning and subsequent analysis for the transport operators or researchers for better and safer management of their transport fleets. The system aimed to have a good precaution and protection on all road users, including drivers, passengers and pedestrians. In turn, it largely saves our community resources, such as the medical and social services consumed on treating the injured persons