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The document describes a drowsy driver warning system that uses cameras to monitor a driver's eyes and lane position. When signs of drowsiness are detected, such as prolonged blinking or drifting out of the lane, the system activates an audible buzzer and tactile massage pad to warn and wake the driver. The system was designed and built by students at the Rochester Institute of Technology as a senior design project.
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Drowsiness detection ppt
The Internet of Things (IoT) is the network of
physical objects devices, vehicles, buildings and
other items embedded with electronics, software,
sensors, and network connectivity that enables
these objects to collect and exchange data.
Starting from small houses to huge industries,
surveillance plays very vital role to fulfill our
safety aspects as Burglary and theft have always
been a problem. In big industries personal security
means monitoring the people’s changing
information like activities, behavior for the purpose
of protecting, managing and influencing
confidential details. Surveillance means watching
over from a distance by means of electronic
equipment such as CCTV cameras but it is costly
for normal residents to set up such kind of system
and also it does not inform the user immediately
when the burglary happens.
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it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
Driver drowsiness detection
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
Automated Driver Fatigue Detection
Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
Driver drowsinees detection and alert.pptx slide
This document describes a drowsiness detection system using deep learning. It aims to develop a prototype that can accurately monitor a driver's eye state in real-time to detect drowsiness and prevent accidents. The system uses a webcam and deep learning to analyze eye images and detect blinking. If drowsiness is detected, it will alert the driver via mobile and limit the vehicle speed. It will also capture an image, send the driver's location to the vehicle owner, and reduce the speed to prevent accidents from drowsy driving. The literature review discusses previous works on using sensors like eye blink detection and their limitations, showing the need for a non-intrusive deep learning-based solution.
Driver DrowsiNess System
The document describes a driver drowsiness detection system that uses image processing and machine learning techniques. The system monitors a driver's eyes for blinking over 1 second to detect drowsiness, then triggers an alert to wake the driver. Such a system could help reduce the number of accidents caused by drowsy driving by alerting drivers when they show signs of fatigue. The system captures the driver's face, recognizes the eyes, detects blinking, and sounds an alarm if drowsiness is detected.
Drowsiness Detection Presentation
Presentation on Drowsiness Detection made for Detecting If a Driver is drunk the project is Python and IOT Based
Real time drowsy driver detection
With the growth in population, the occurrence of automobile accidents has also seen an
increase. A detailed analysis shows that, around half million accidents occur in a year , in India
alone. Further , around 60% of these accidents are caused due to driver fatigue. Driver fatigue
affects the driving ability in the following 3 areas, a) It impairs coordination, b) It causes longer
reaction times, and, c)It impairs judgment. Through this paper, we provide a real time
monitoring system using image processing, face/eye detection techniques. Further, to ensure
real-time computation, Haarcascade samples are used to differentiate between an eye blink and
drowsy/fatigue detection.
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Drowsiness detection ppt
The Internet of Things (IoT) is the network of
physical objects devices, vehicles, buildings and
other items embedded with electronics, software,
sensors, and network connectivity that enables
these objects to collect and exchange data.
Starting from small houses to huge industries,
surveillance plays very vital role to fulfill our
safety aspects as Burglary and theft have always
been a problem. In big industries personal security
means monitoring the people’s changing
information like activities, behavior for the purpose
of protecting, managing and influencing
confidential details. Surveillance means watching
over from a distance by means of electronic
equipment such as CCTV cameras but it is costly
for normal residents to set up such kind of system
and also it does not inform the user immediately
when the burglary happens.
Driver fatigue detection system
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
Driver drowsiness detection
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
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Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
Driver drowsinees detection and alert.pptx slide
This document describes a drowsiness detection system using deep learning. It aims to develop a prototype that can accurately monitor a driver's eye state in real-time to detect drowsiness and prevent accidents. The system uses a webcam and deep learning to analyze eye images and detect blinking. If drowsiness is detected, it will alert the driver via mobile and limit the vehicle speed. It will also capture an image, send the driver's location to the vehicle owner, and reduce the speed to prevent accidents from drowsy driving. The literature review discusses previous works on using sensors like eye blink detection and their limitations, showing the need for a non-intrusive deep learning-based solution.
Driver DrowsiNess System
The document describes a driver drowsiness detection system that uses image processing and machine learning techniques. The system monitors a driver's eyes for blinking over 1 second to detect drowsiness, then triggers an alert to wake the driver. Such a system could help reduce the number of accidents caused by drowsy driving by alerting drivers when they show signs of fatigue. The system captures the driver's face, recognizes the eyes, detects blinking, and sounds an alarm if drowsiness is detected.
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Presentation on Drowsiness Detection made for Detecting If a Driver is drunk the project is Python and IOT Based
Real time drowsy driver detection
With the growth in population, the occurrence of automobile accidents has also seen an
increase. A detailed analysis shows that, around half million accidents occur in a year , in India
alone. Further , around 60% of these accidents are caused due to driver fatigue. Driver fatigue
affects the driving ability in the following 3 areas, a) It impairs coordination, b) It causes longer
reaction times, and, c)It impairs judgment. Through this paper, we provide a real time
monitoring system using image processing, face/eye detection techniques. Further, to ensure
real-time computation, Haarcascade samples are used to differentiate between an eye blink and
drowsy/fatigue detection.
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This Presentation is on the topic of Driver drowsiness Detection .
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Vision based system for monitoring the loss of attention in automotive driver
This document describes a vision-based system for monitoring driver attention and drowsiness in automobiles. It discusses using a camera and image processing techniques like OpenCV, Haar classifiers, and template matching to detect when a driver's eyes are closed, indicating loss of attention. The system is implemented on a Raspberry Pi board using Raspbian OS. Eye detection is done using a Haar classifier trained on eye images. Template matching is also used to track eye position. When the eyes are detected as closed for too long and head position exceeds thresholds from an accelerometer, a buzzer is activated to alert the driver. The goal is to develop a low-cost drowsiness detection system for improving road safety.
Driver detection system_final.ppt
The document presents a seminar on driver drowsiness detection. It discusses the increasing problem of accidents due to drowsy driving and outlines the objectives of building a system to detect driver drowsiness in real-time through monitoring eye blinks and alerting the driver. The proposed methodology uses a behavioral approach including eye detection, blink counting, and analysis to determine drowsiness levels and provide alerts or vehicle control interventions if needed.
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Drowsiness is a critical factor impairing drivers’ performance in driving safely. There are several approaches in dealing with this issue based on human-machine interaction to detect drivers’ dozing off state, and then alert them to keep awake by sound or visual. These techniques fundamentally measure driver’s physical changes such as head angle, fatigue level and eyes states which are the indicators of drowsy state. However, they are limited in providing accurate and reliable results. Therefore, the project aims to achieve higher accuracy rate of drowsiness detection by using a very potential technology, electroencephalography (EEG) which is used widely in medical areas. Other than providing reliable result, the final product would bring more conveniences for customers with portability, easy-to-deploy and multi-device compatibility feature. In this project, its methodology first shows the strong correlation between drowsy state with brainwave frequency. Then a proposed system and testing plan are suggested based on the project objectives and available technologies. The final product is simply comprised of a hat with attached small electronic package used to record brainwave and a handheld device placing on dashboard of the car with an installed app. Finally, project management section will present in detail the human resources, scheduling, budget plan and risk analysis to show how it will be going to complete the project in six months.
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Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
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https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
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Driver Drowsiness is a grave issue resulting in many road accidents each year. To evaluate the exact number of sleep related accidents because of the difficulties in detecting whether fatigue was a factor and in assessing the level of fatigue is not currently possible. In this paper the camera will be placed besides the rare view mirror of car in way such that it is in clear view of the frontal face of the driver. This camera will continuously capture the video of driver’s frontal face while driving. The system will detect the frontal face in the image and later the eyes. Depending upon the conditions the system will generate an alert. The focus will be on the 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 to avoid accidents.
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Abstract: Now-a-days so many road accidents occur due to driver distraction while he is driving. Those accidents are broadly depends upon wide range of driver state such as drowsy state, alcoholic state, depressed state etc. Even driver distraction and conversation with passengers during driving can lead in major problems. To address the problem we propose a Driver fatigue Monitoring and
warning system based on eye-tracking, which is consider as active safety system. This system is useful and helpful for drivers to be alert while driving. Eye tracking is one of the major technologies for future driver system since human eyes contains much information. Sleepiness reduces reaction time of safe driving. The driver distraction is measured by the person eye closure rate for certain period while driving. It is implemented by comparing the image extracted from video and the video that is currently
performing. The percentage of eyes is compared from both the frames, if the driver is suspected to be sleeping then a warning alarm is given to alert the driver
Drowsiness detection and updating system
This document summarizes a drowsiness detection system called Technorax 5.0 created by team Bit_Rebels. It discusses how drowsy driving is a major cause of accidents in India, accounting for 20% of fatal crashes. The system uses computer vision techniques like facial landmark detection and eye aspect ratio analysis to monitor a driver for signs of drowsiness like prolonged blinking or eye closures. If such signs are detected, an alarm is triggered to alert the driver and attempt to prevent potential accidents from drowsy driving.
Iris scanning
This document summarizes the history and technology of iris recognition biometrics. It discusses how the iris was first proposed as a biometric in the 1930s, was featured in Bond films in the 1980s, and was patented and implemented in ATMs in the late 1980s. The iris is described as a protected internal organ that has many unique identifying features, making it very difficult to artificially duplicate. The document outlines the major parts of an iris recognition system and discusses many applications of the technology such as computer login, border control, and forensics. It concludes that iris recognition is extremely accurate and could be used for large-scale identification.
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This document discusses the history and development of facial recognition systems. It describes how pioneers in the 1960s began developing early systems using graphics tablets, and the challenges of accounting for variability in lighting, expression, and other factors. The document outlines different types of current facial recognition approaches, including traditional 2D recognition and emerging 3D recognition techniques. It provides examples of software using facial recognition and potential applications that have been developed or could be developed. A survey of Hong Kong citizens found facial recognition is not very common but many would be interested in using it on computers or for access control. The conclusion discusses both benefits and privacy concerns of the technology.
Saksham seminar report
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This slide is about Artificial passenger for Technical seminar presentation it will help you in better way to present
Vehicle Detection using Camera
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B0151017
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Driver gaze tracking and eyes off the road detection system
I3E Technologies,
#23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
MINI-PROJECTS AVAILABLE.
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This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
Drowsy Driver detection system
all the project contents regarding drowsy driver detection system with complete description are given. happy watching
Real time detecting driver’s drowsiness using computer vision
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Vision based system for monitoring the loss of attention in automotive driver
This document describes a vision-based system for monitoring driver attention and drowsiness in automobiles. It discusses using a camera and image processing techniques like OpenCV, Haar classifiers, and template matching to detect when a driver's eyes are closed, indicating loss of attention. The system is implemented on a Raspberry Pi board using Raspbian OS. Eye detection is done using a Haar classifier trained on eye images. Template matching is also used to track eye position. When the eyes are detected as closed for too long and head position exceeds thresholds from an accelerometer, a buzzer is activated to alert the driver. The goal is to develop a low-cost drowsiness detection system for improving road safety.
Driver detection system_final.ppt
The document presents a seminar on driver drowsiness detection. It discusses the increasing problem of accidents due to drowsy driving and outlines the objectives of building a system to detect driver drowsiness in real-time through monitoring eye blinks and alerting the driver. The proposed methodology uses a behavioral approach including eye detection, blink counting, and analysis to determine drowsiness levels and provide alerts or vehicle control interventions if needed.
Anti drowsy alarm for drivers
Drowsiness is a critical factor impairing drivers’ performance in driving safely. There are several approaches in dealing with this issue based on human-machine interaction to detect drivers’ dozing off state, and then alert them to keep awake by sound or visual. These techniques fundamentally measure driver’s physical changes such as head angle, fatigue level and eyes states which are the indicators of drowsy state. However, they are limited in providing accurate and reliable results. Therefore, the project aims to achieve higher accuracy rate of drowsiness detection by using a very potential technology, electroencephalography (EEG) which is used widely in medical areas. Other than providing reliable result, the final product would bring more conveniences for customers with portability, easy-to-deploy and multi-device compatibility feature. In this project, its methodology first shows the strong correlation between drowsy state with brainwave frequency. Then a proposed system and testing plan are suggested based on the project objectives and available technologies. The final product is simply comprised of a hat with attached small electronic package used to record brainwave and a handheld device placing on dashboard of the car with an installed app. Finally, project management section will present in detail the human resources, scheduling, budget plan and risk analysis to show how it will be going to complete the project in six months.
Face recognition
Face recognition is a biometric technique that uses unique facial measurements to identify or verify individuals in images. It analyzes the shape, pattern, and positioning of facial features. Face recognition systems first detect faces in images, then extract distinguishing nodal points like eye depth, nose width, and distance between eyes. They compare these measurements to templates stored in a database to identify matches. While convenient and non-invasive, face recognition has limitations like inability to distinguish identical twins and decreased accuracy with changes in appearance. It finds applications in security, law enforcement, and commercial uses like building access control and ATMs.
Self Driving Autopilot Car
This document discusses self-driving or autonomous vehicles. It provides an introduction to autonomous cars and their ability to sense surroundings using sensors and computer vision. The document outlines some of the technologies used in autonomous vehicles, including radar, lidar, GPS, cameras, ultrasonic sensors and more. It describes how components like ABS, electronic stability control, adaptive high beams, night vision and parking sensors contribute to autonomous functionality. The document discusses advantages such as reduced accidents and improved mobility for disabled/elderly, as well as disadvantages including job loss and hacking risks. It concludes that autonomous vehicles could significantly reduce traffic and avoid accidents by 2020.
autonomous car
The document presents information on driverless cars, including a brief history of autonomous vehicles from 1969. It discusses how driverless cars use sensors and software to navigate without human input, and considers issues like safety, economic impacts, and how autonomous technology could affect transportation. While driverless cars may reduce accidents and open up new mobility options, challenges include high costs, job disruption, and ensuring computer systems don't cause crashes. The conclusion discusses both benefits of and social barriers to autonomous vehicle adoption.
Google Driverless Cars
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The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
DRIVER DROWSINESS ALERT SYSTEM
Driver Drowsiness is a grave issue resulting in many road accidents each year. To evaluate the exact number of sleep related accidents because of the difficulties in detecting whether fatigue was a factor and in assessing the level of fatigue is not currently possible. In this paper the camera will be placed besides the rare view mirror of car in way such that it is in clear view of the frontal face of the driver. This camera will continuously capture the video of driver’s frontal face while driving. The system will detect the frontal face in the image and later the eyes. Depending upon the conditions the system will generate an alert. The focus will be on the 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 to avoid accidents.
Driver Fatigue Monitoring System Using Eye Closure
Abstract: Now-a-days so many road accidents occur due to driver distraction while he is driving. Those accidents are broadly depends upon wide range of driver state such as drowsy state, alcoholic state, depressed state etc. Even driver distraction and conversation with passengers during driving can lead in major problems. To address the problem we propose a Driver fatigue Monitoring and
warning system based on eye-tracking, which is consider as active safety system. This system is useful and helpful for drivers to be alert while driving. Eye tracking is one of the major technologies for future driver system since human eyes contains much information. Sleepiness reduces reaction time of safe driving. The driver distraction is measured by the person eye closure rate for certain period while driving. It is implemented by comparing the image extracted from video and the video that is currently
performing. The percentage of eyes is compared from both the frames, if the driver is suspected to be sleeping then a warning alarm is given to alert the driver
Drowsiness detection and updating system
This document summarizes a drowsiness detection system called Technorax 5.0 created by team Bit_Rebels. It discusses how drowsy driving is a major cause of accidents in India, accounting for 20% of fatal crashes. The system uses computer vision techniques like facial landmark detection and eye aspect ratio analysis to monitor a driver for signs of drowsiness like prolonged blinking or eye closures. If such signs are detected, an alarm is triggered to alert the driver and attempt to prevent potential accidents from drowsy driving.
Iris scanning
This document summarizes the history and technology of iris recognition biometrics. It discusses how the iris was first proposed as a biometric in the 1930s, was featured in Bond films in the 1980s, and was patented and implemented in ATMs in the late 1980s. The iris is described as a protected internal organ that has many unique identifying features, making it very difficult to artificially duplicate. The document outlines the major parts of an iris recognition system and discusses many applications of the technology such as computer login, border control, and forensics. It concludes that iris recognition is extremely accurate and could be used for large-scale identification.
Facial Recognition System
Facial recognition systems have advanced significantly since first being developed in the 1960s. The software measures distinguishing landmarks on faces such as distance between eyes, nose width, and jawline to create a unique "faceprint". Emerging 3D facial recognition is more accurate as it can recognize faces from different angles. Current uses include law enforcement matching faces in crowds and at borders. Future applications may include employee time tracking and prison visitor screening.
Facial powerpoint
This document discusses the history and development of facial recognition systems. It describes how pioneers in the 1960s began developing early systems using graphics tablets, and the challenges of accounting for variability in lighting, expression, and other factors. The document outlines different types of current facial recognition approaches, including traditional 2D recognition and emerging 3D recognition techniques. It provides examples of software using facial recognition and potential applications that have been developed or could be developed. A survey of Hong Kong citizens found facial recognition is not very common but many would be interested in using it on computers or for access control. The conclusion discusses both benefits and privacy concerns of the technology.
Saksham seminar report
The document is a seminar report on the topic of computer vision. It provides an overview of computer vision, discussing its history, applications in various fields like robotics, medicine, security, transportation, and human-computer interaction. It also describes the challenges of computer vision as an inverse problem and provides examples of computer vision systems used in domains like industrial automation, image databases, and more.
Artificial Passenger
This slide is about Artificial passenger for Technical seminar presentation it will help you in better way to present
Vehicle Detection using Camera
Vehicle Detection using Camera
Vehicle Detection Using Cameras for Self-Driving Cars |
Using machine learning and computer vision I create a pipeline that detects nearby vehicles from a dash-cam.
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Driver gaze tracking and eyes off the road detection system
I3E Technologies,
#23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
MINI-PROJECTS AVAILABLE.
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This document describes a real-time driver drowsiness detection system using an ARM9 microcontroller. The system uses a webcam to capture images of the driver's eyes and an electrooculography (EOG) sensor to monitor visual activity. Image processing techniques are used to detect eye closure and blinking patterns. If drowsiness is detected, an alarm is activated to warn the driver. The system was tested on 15 people with 80% accuracy. The document concludes that image processing provides a non-invasive way to accurately detect drowsiness without interfering with the driver.
Yawning analysis for driver drowsiness detection
This document discusses a system for detecting driver drowsiness through yawning analysis. The system first detects the driver's face using skin detection in the YCbCr color space. It then tracks the face using canny edge detection and detects the eyes and mouth using Haar-like features. Yawning detection is performed by analyzing changes in mouth geometry, such as an increase in mouth pixel count during yawning. If yawning is detected, the system will alert the driver to prevent accidents caused by drowsiness. The goal is to help reduce the estimated 1550 deaths and $12.5 billion in losses caused by fatigue-related crashes each year.
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redundancies. This work proposes a switching pattern to improve
the performance of chosen H-bridge type CCMLI over
conventional CCMLI. The PWM technique used in this work is
Phase Opposition Disposition PWM(PODPWM). The
performance of proposed H-bridge type CCMLI is verified
through MATLAB-Simulink based simulation. It has been
observed that the THD is low in chosen CCMLI compared to
conventional CCMLI.EYE TRACKING BASED DRIVER DROWSINESS MONITORING AND WARNING SYSTEM
EYE TRACKING BASED DRIVER DROWSINESS MONITORING AND WARNING SYSTEMInternational Journal of Technical Research & Application
This project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.Viewers also liked (7)
Driver gaze tracking and eyes off the road detection system
Driver gaze tracking and eyes off the road detection system
LI-ION BATTERY TESTING FROM MANUFACTURING TO OPERATION PROCESS
LI-ION BATTERY TESTING FROM MANUFACTURING TO OPERATION PROCESS
POD-PWM BASED CAPACITOR CLAMPED MULTILEVEL INVERTER
POD-PWM BASED CAPACITOR CLAMPED MULTILEVEL INVERTER
EYE TRACKING BASED DRIVER DROWSINESS MONITORING AND WARNING SYSTEM
EYE TRACKING BASED DRIVER DROWSINESS MONITORING AND WARNING SYSTEM
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Automatic gate control
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Suppose a vehicle needs to be parked and enters a parking area without any prior knowledge of availability of vacant space for parking.
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- 1. Drowsy Driver Warning System Mark Cataldi, Jesse Harvey, Dieter LaskowskiSenior Design Projects II – Fall 2008Department of ComputerEngineering, Rochester Instituteof Technology, Rochester, NY Project Description Drowsiness slows reaction time, decreases awareness, and impairs judgment just like drugs or alcohol. Independent studies1 of the Pennsylvania Turnpike and the New York State Thruway estimate that 50 percent of fatal crashes on those roads are caused by drowsy drivers. Many people would never consider drinking and driving, but many fail to recognize that driving drowsy can be just as fatal as driving drunk. The Drowsy Driver Warning System is designed to prevent a driver from falling asleep at the wheel. Two cameras are mounted inside the automobile cabin. One of the cameras faces the driver in order to monitor the driver’s eyes. The other camera faces the front windshield of the automobile in order to monitor lane position. When the system determines that the driver is becoming drowsy or the automobile is drifting out of its current lane, the warning system will enable an audible buzzer and massage pad in order to awaken the driver. Screen capture of the Lane Departure Detector after Sobel edge detection (not shown) and Hough line transform. The Hough lines are represented by the green lines drawn from the origin, which are then decoded into the lane edges and drawn in red. To eliminate extraneous data, only edges in the area between the two horizontal lines are analyzed. The crossbar (pink) shows the region which is considered a dangerous lane departure. If a Hough line passes through the cross bar it will trigger an alarm message to be sent. Screen capture of the blink detection application supplied by the Machine Perception Toolbox. The red box detects a face found by the application with white boxes around the eyes. The red bar to the left shows the blink threshold which will grow or shrink based on how likely it is that the subject is blinking. The bar will turn blue if a blink is detected. After a specified frequency of blinks, the blink detector will activate the warning system. Software Hardware Cameras: The Logitech QuickCam® Pro 9000 was selected for it’s premium autofocus and ultra-wide field of view in hopes of providing the best possible input to the image processing libraries. The IR blocking filter on the driver facing camera wasphysically removed to allow for IR illumination. When a hazardous situation is detected, the warning system will provide both a tactile and audible response in order to wake the drowsy driver. The audible response is driven by a Piezo buzzer operating in the 70 - 80 dB range. The tactile response is provided by a USB massage pad that has been modified to accept control signals from the microcontroller. Infrared LEDs are illuminated when MPT is unable to detect the driver’s face using ambient lighting. This enables face monitoring at night or under low light situations. The IR lighting also helps reduce glare on eyeglass lenses. A Freescale HCS12 microcontroller is responsible for activating the warning system, enabling the IR LEDs, and interfacing with the driver control box. A driver control box is provided to allow the driver to inhibit face and lane detection independently and simultaneously. Status LEDs are also mounted in the control box to notify the driver if blink or lane departure detection are not functioning reliably. Image processing is handled by two software libraries: Machine Perception Toolbox (MPT) and a custom lane departure detection application that utilizes the AForge.Net framework. The Machine Perception Toolbox2 (MPT) supplies cross-platform libraries for real-time perception primitives, including face detection, eye detection, blink detection and color tracking. AForge.NET3 is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence. It includes image processing, neural networks, genetic algorithms, machine learning, and much more. A DrowsyDriver C++ library was created to interface with the different image processing libraries and the microcontroller. When the application launches, DrowsyDriver initializes both MPT and the lane departure application. DrowsyDriver then monitors the image processing results and communicates its status with the microcontroller through PC RS-232 serial port. The code running on the microcontroller was written in MCU12 assembly. The microcontroller reads the serial port and interprets the data to manipulate specific output ports and track the warning system’s status. The microcontroller also reads the input values from the driver control box in order to inhibit or enable the proper alerts. The Team Project Costs Imaging System Warning & Support System Acknowledgements/Resources Special Thanks The team would like to thank Dr. Roy Czernikowski for his assistance and guidance throughout the project. The team would also like to thank Richard Tolleson for his continued technical support and miscellaneous project supplies. The team recognizes Michael Snook for his assistance in the area of circuit design and analysis of the team’s electrical components. Finally the team would like to show its appreciation towards their classmates for their positive feedback and suggestions, which ultimately contributed to the success of the project. Resources 1: http://www.sleepfoundation.org/site/apps/nlnet/content3.aspx?c=huIXKjM0IxF&b=2464479&ct=3445645 2: MPT: http://mplab.ucsd.edu/grants/project1/free-software/mptwebsite/introduction.html 3: AForge: http://code.google.com/p/aforge Team members from left to right: Mark Cataldi, Jesse Harvey and Dieter Laskowski