Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents is attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist can warn of inattentiveness and drowsiness in an extended speed range and notify drivers of their current state of fatigue and the driving time since the last break, offers adjustable sensitivity and, if a warning is emitted, indicates nearby service areas in the COMMAND navigation system.
Driver drowsiness detection is a car safety technology that prevents accidents when the driver is getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads. 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.
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 effects. Driver inattention might be the result of a lack of alertness when driving due to driver drowsiness and distraction. Driver distraction occurs when an object or event draws a person’s attention away from the driving task.
Unlike driver distraction, driver drowsiness involves no triggering event but, instead, is characterized by a progressive withdrawal of attention from the road and traffic demands.
Both driver drowsiness and distraction, however, might have the same effects, i.e., decreased driving performance, longer reaction time, and an increased risk of crash involvement. Based on the Acquisition of video from the camera that is in front of driver perform real-time processing of an incoming video stream in order to infer the driver’s level of fatigue if the drowsiness is Estimated then the output is send to the alarm system and alarm is activated.
• Drowsiness detection is based on these three parameters. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system.
• Vehicle based measures: A number of metrics, including deviations from lane position, movement of the steering wheel, pressure on the acceleration pedal, etc., are constantly monitored and any change in these that crosses a specified threshold indicates a significantly increased probability that the driver is drowsy.
• Behavioural-based measures: The behaviour of the driver, including yawning, eye closure, eye blinking, head pose, etc. is monitored through a camera and the driver is alerted if any of these drowsiness symptoms are detected. Physiological based measures: The correlation between physiological signals ECG (Electrocardiogram) and EOG (Electrooculogram). Drowsiness is detected through pulse rate, heartbeat and brain information.
o Local binary patterns (LBPs) have aroused increasing interest in image processing and computer vision. As a nonparametric method, LBP summarizes local structures of images efficiently by comparing each pixel with its neighbouring pixels.
o The most important properties of LBP are its tolerance regarding monotonic illumination changes and its computational simplicity. This technique is mostly used for detecting emotions on the face like, happiness, sadness, excitement etc.
o LBP (local binary pattern) is used in drowsiness detection for detecting face of the driver, it divides the image into four quadrants then the top and bottom part are detected. Shows LBP extract the image from the video then the image is divided into blocks, after that LBP histogram are generated from the each block and feature histograms are formed.
Block Diagram of overall System
The current study was designed to provide further information for traffic safety and others could use in their efforts to reduce the number of drowsy related crashes. The study had the following principles: 1- to verify message that need to be conveyed.
Why are these people in drowsy-related crashes? Is it due to long sleep or is minor sleep the bigger problem? What do person already know and practice with regard to drowsy driving? 2-to examine potential under-reporting of drowsy-related crashes. The literature suggest that drowsy-related crashes are underreported by law enforcement officers. We wanted to examine and study the extent to which drivers statements corroborated the police report.
Eye blink detection system
In detection process eyelid detection, eye training, NIR image, LBP features, histogram algorithms are used. By using this algorithm system can detect eyelid, position of eye on face , its nearest image recognition, its least binary features and histogram for detecting whether eyes are open or closed.
If the system found that numbers of frames having similar images in which the person’s eyes are closed occur sequentially, then the system will play a alarm in the form of sound. The process of deactivating the alarm in manual and not automatic. The user should manually close the alarm.
HARDWARE AND SOFTWARE REQUIREMENT:
Operating System : WINDOWS
Simulation Tool : OPENCV PYTHON
Documentation : Ms-Office
CPU type : Intel Pentium 4
Clock speed : 3.0 GHz
Ram size : 512 MB
Hard disk capacity : 80 GB
Monitor type : 15 Inch colour monitor
Keyboard type : Internet keyboard
CD -drive type : 52xmax
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