Real-time Intelligent Alert System on Driver’s Hypo-Vigilance Detection Using Template Matching Technique

According to experts, anyone who do not take break after long period of driving task can cause weariness. This system is one of the major approaches for preventing accidents by fatigue detection and distraction detection. Since there are many systems are available for warning the drivers. As fatigue is the main reason for accidents as well as distraction of drivers especially in the highways and rural areas. Because fatigue reduces driver’s perceptions and decision making capability to control the vehicle. This results irritation and the person is no longer paying attention in driving. In this paper, method for face detection and eye tracking from human face image is used. We have discussed method for determining eye template of open eyes and closed eyes. It is based on real-time acquisition of a driver’s face images and template matching method is applied to extract hypo-vigilance symptoms. DOI: 10.17762/ijritcc2321-8169.15025

Drowsiness detection for car assisted driver system using image processing analysis-interfacing with hardware

The purpose of this study is to detect drowsiness in drivers to prevent accidents and to improve safety on the highways. A method for detecting drowsiness/sleepiness in drivers is developed by using a camera that point directly towards the driver’s face and capture for the video. Once the video is captured, monitoring the face region and eyes in order to detect drowsy/fatigue. The system able to monitoring eyes and determines whether the eyes are in an open position or closed state. In such a case when drowsy is detected, a warning signal is issued to alert the driver. It can determine a time proportion of eye closure as the proportion of a time interval that the eye is in the closed position. If the driver’s eyes are closed cumulatively more than a standard value, the system draws the conclusion that the driver is falling asleep, and then it will activate an alarm sound to alert the drive

Determining the relative position of vehicles considering bidirectional traffic scenarios in VANETS

Researchers pertaining to both academia and industry have shown strong interest in developing and improving the existing critical ITS solutions. In some of the existing solutions, specially the ones that aim at providing context aware services, the knowledge of relative positioning of one node by other nodes becomes crucial. In this paper we explore, apart from the conventional use of GPS data, the applicability of image processing to aid in determining the relative positions of nodes in a vehicular network. Experiments conducted show that both the use of location information and image processing works well and can be deployed depending on the requirement of the application. Our experiments show that the results that used location information were affected by GPS errors, while the use of image processing, although producing more accurate results, require significantly more processing power

Drive in Peace

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in recent years by many scholars and used extensively in various fields, for example, driver activity tracking, driver visual attention monitoring, and in-car camera systems.In this paper, we use the Windows operating system as the development environment, and utilize PC as the hardware platform. First, the system uses a camera to obtain the frame with a human face to detect, and then uses the frame to set the appropriate skin color scope to find face. Next, we find and mark out the eyes and the lips from the selected face area. Finally, we combine the image processing of eyes features with fuzzy logic to determine the driver's fatigue level, and make the graphical man-machine interface with MiniGUI for users to operate.Along with that we are using Arduino Uno microcontroller which is connected to MQ2-smoke sensor through which we can detect smoke which appears through issue in the car system. The results of experiment show that we achieve this system on PC platform successfully

Driver fatigue monitoring system using support vector machines

Driver fatigue is one of the leading causes of traffic accidents. This paper presents a real-time non-intrusive fatigue monitoring system which exploits the driver's facial expression to detect and alert fatigued drivers. The presented approach adopts the Viola-Jones classifier to detect the driver's facial features. The correlation coefficient template matching method is then applied to derive the state of each feature on a frame by frame basis. A Support Vector Machine (SVM) is finally integrated within the system to classify the facial appearance as either fatigued or otherwise. Using this simple and cheap implementation, the overall system achieved an accuracy of 95.2%, outperforming other developed systems employing expensive hardware to reach the same objective.peer-reviewe

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