12-06-2011, 02:23 PM
Iam an ece student.Looking for final year projects.i need the circuit diagram and description of the drowsy driver detection project.
12-06-2011, 02:23 PM
Iam an ece student.Looking for final year projects.i need the circuit diagram and description of the drowsy driver detection project.
03-03-2012, 12:26 PM
to get information about the topic drowsy driver detection full report ppt and related topic refer the link bellow
https://seminarproject.net/Thread-a-drow...ity-system
03-05-2012, 05:45 PM
hi can anyone help with a coding of drowsiness detection in matlab? i have to detect the eye state of the user to determine drowsiness of they are closed for a certain period. please reply to me anyone who can assist me in that: neeravhurry[at]hotmail.com
23-07-2012, 10:18 AM
Drowsy Driver Detection System
Drowsy Driver Detection.pdf (Size: 911.02 KB / Downloads: 230) Abstract A Drowsy Driver Detection System has been developed, using a non-intrusive machine vision based concepts. The system uses a small monochrome security camera that points directly towards the driver’s face and monitors the driver’s eyes in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to alert the driver. This report describes how to find the eyes, and also how to determine if the eyes are open or closed. The algorithm developed is unique to any currently published papers, which was a primary objective of the project. The system deals with using information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. Once the face area is found, the eyes are found by computing the horizontal averages in the area. Taking into account the knowledge that eye regions in the face present great intensity changes, the eyes are located by finding the significant intensity changes in the face. Once the eyes are located, measuring the distances between the intensity changes in the eye area determine whether the eyes are open or closed. A large distance corresponds to eye closure. If the eyes are found closed for 5 consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal. The system is also able to detect when the eyes cannot be found, and works under reasonable lighting conditions. Introduction Driver fatigue is a significant factor in a large number of vehicle accidents. Recent statistics estimate that annually 1,200 deaths and 76,000 injuries can be attributed to fatigue related crashes [9]. The development of technologies for detecting or preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. 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 prototype 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 in real-time. By monitoring the eyes, it is believed that the symptoms of driver fatigue can be detected early enough to avoid a car accident. Detection of fatigue involves a sequence of images of a face, and the observation of eye movements and blink patterns. Report Organization The documentation for this project consists of 10 chapters. Chapter 4 represents the Literature Review, which serves as an introduction to current research on driver drowsiness detection systems. Chapter 5 discusses the design issues of the project, specifically, the issues and concepts behind real-time image processing. Following this, Chapter 6 describes the design of the system, and chapter 7 describes the algorithm behind the system. Chapter 8 gives additional information of the real-time system and the challenges met. Chapter 9 shows images illustrating the steps taken in localizing the eyes, which is followed by a discussion on some possible future directions for the project, after which the final conclusions are drawn in chapter 10. Monitoring Physiological Characteristics Among these methods, the techniques that are best, based on accuracy are the ones based on human physiological phenomena [9]. This technique is implemented in two ways: measuring changes in physiological signals, such as brain waves, heart rate, and eye blinking; and measuring physical changes such as sagging posture, leaning of the driver’s head and the open/closed states of the eyes [9]. The first technique, while most accurate, is not realistic, since sensing electrodes would have to be attached directly onto the driver’s body, and hence be annoying and distracting to the driver. In addition, long time driving would result in perspiration on the sensors, diminishing their ability to monitor accurately. The second technique is well suited for real world driving conditions since it can be non-intrusive by using optical sensors of video cameras to detect changes. Other Methods Driver operation and vehicle behaviour can be implemented by monitoring the steering wheel movement, accelerator or brake patterns, vehicle speed, lateral acceleration, and lateral displacement. These too are non-intrusive ways of detecting drowsiness, but are limited to vehicle type and driver conditions. Face Top and Width Detection The next step in the eye detection function is determining the top and side of the driver’s face. This is important since finding the outline of the face narrows down the region in which the eyes are, which makes it easier (computationally) to localize the position of the eyes. The first step is to find the top of the face. The first step is to find a starting point on the face, followed by decrementing the y-coordinates until the top of the face is detected. Assuming that the person’s face is approximately in the centre of the image, the initial starting point used is (100,240). The starting x-coordinate of 100 was chosen, to insure that the starting point is a black pixel (no on the face). The following algorithm describes how to find the actual starting point on the face, which will be used to find the top of the face. Conclusion A non-invasive system to localize the eyes and monitor fatigue was developed. Information about the head and eyes position is obtained though various self-developed image processing algorithms. During the monitoring, the system is able to decide if the eyes are opened or closed. When the eyes have been closed for too long, a warning signal is issued. In addition, during monitoring, the system is able to automatically detect any eye localizing error that might have occurred. In case of this type of error, the system is able to recover and properly localize the eyes. The following conclusions were made.
12-02-2013, 02:56 PM
i need a ppt on drowsy driver detection for my final year project report.. |
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