Driver drowsiness detection
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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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Drowsiness Detection Presentation
Presentation on Drowsiness Detection made for Detecting If a Driver is drunk the project is Python and IOT Based
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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.
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.
Driver Drowsiness Detection report
This document describes a student project to develop a driver drowsiness detection system using OpenCV and Python. It includes approval from an internal examiner, declarations by the student, and certificates of completion. The system detects drowsiness based on eye closure and yawning detection using facial landmark tracking and thresholds on eye and mouth aspect ratios. Experimental results showed the system could successfully detect drowsiness and provide alerts when thresholds were exceeded.
Driver Drowsiness Detection Review
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 Detection using machine learning (1).pptx
The document describes a proposed system to detect driver drowsiness using OpenCV and machine learning techniques. The system would use computer vision and facial landmark detection on video from an in-vehicle camera to monitor the driver's eyes and mouth for signs of fatigue like blinking rate, yawning and prolonged eye closures. If drowsiness is detected, the system will alert the driver with an alarm sound and may also activate a self-driving mode if the driver's eyes are closed for over 60 seconds. The proposed system aims to reduce accidents caused by fatigued driving and promote road safety.
automatic number plate recognition
Automatic number plate recognition (ANPR) uses optical character recognition on images to read vehicle registration plates. It has seven elements: cameras, illumination, frame grabbers, computers, software, hardware, and databases. ANPR detects vehicles, captures plate images, and processes the images to recognize plates. It has advantages like improving safety and reducing crime. Applications include parking, access control, tolling, border control, and traffic monitoring.
Download as PPTX - PowerPoint Presentation
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 Presentation
Presentation on Drowsiness Detection made for Detecting If a Driver is drunk the project is Python and IOT Based
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.
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.
Driver Drowsiness Detection report
This document describes a student project to develop a driver drowsiness detection system using OpenCV and Python. It includes approval from an internal examiner, declarations by the student, and certificates of completion. The system detects drowsiness based on eye closure and yawning detection using facial landmark tracking and thresholds on eye and mouth aspect ratios. Experimental results showed the system could successfully detect drowsiness and provide alerts when thresholds were exceeded.
Driver Drowsiness Detection Review
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 Detection using machine learning (1).pptx
The document describes a proposed system to detect driver drowsiness using OpenCV and machine learning techniques. The system would use computer vision and facial landmark detection on video from an in-vehicle camera to monitor the driver's eyes and mouth for signs of fatigue like blinking rate, yawning and prolonged eye closures. If drowsiness is detected, the system will alert the driver with an alarm sound and may also activate a self-driving mode if the driver's eyes are closed for over 60 seconds. The proposed system aims to reduce accidents caused by fatigued driving and promote road safety.
automatic number plate recognition
Automatic number plate recognition (ANPR) uses optical character recognition on images to read vehicle registration plates. It has seven elements: cameras, illumination, frame grabbers, computers, software, hardware, and databases. ANPR detects vehicles, captures plate images, and processes the images to recognize plates. It has advantages like improving safety and reducing crime. Applications include parking, access control, tolling, border control, and traffic monitoring.
Download as PPTX - PowerPoint Presentation
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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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.
ACCIDENT DETECTION AND VEHICLE TRACKING USING GPS,GSM AND MEMS
This document describes a vehicle accident detection and tracking system using GPS, GSM, and MEMS sensors. The system detects accidents using a MEMS sensor and then uses GPS to determine the vehicle's location. The location is sent via GSM to emergency services and authorized contacts to provide rapid response. The system aims to quickly locate accident sites and notify help in remote areas with limited communication infrastructure.
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The project is designed to develop a density based dynamic traffic signal system having remote override facilities. During normal time the signal timing changes automatically on sensing the traffic density at the junction but in the event of any emergency vehicle like ambulance, fire brigade etc. requiring priority are built in with RF remote control to override the set timing by providing instantaneous green signal in the desired direction while blocking the other lanes by red signal for some time. Traffic congestion is a severe problem in many major cities across the world thus it is felt imperative to provide such facilities to important vehicles.
Conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density. Junction timings allotted are fixed. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time. The proposed system using a PIC microcontroller duly interfaced with sensors, changes the junction timing automatically to accommodate movement of vehicles smoothly avoiding unnecessary waiting time at the junction. The sensors used in this project are IR, are in line of sight configuration across the loads to detect the density at the traffic signal. The override feature is activated by an on board RF transmitter operated from the emergency vehicle.
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.
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This document describes the implementation of a smart helmet system using solar power. The system includes an alcohol sensor, temperature sensor, vibration sensor, GPS module, GSM module, and microcontroller to provide safety features like detecting accidents, monitoring alcohol levels, tracking location, and regulating temperature. The smart helmet aims to reduce accidents by preventing intoxicated riding and automatically alerting emergency contacts in the event of a crash. It can also be used to remotely immobilize a vehicle if the helmet is removed or stolen. The system design uses low power components like a solar cell to allow for portable operation.
Driver drowsiness monitoring system using visual behavior and Machine Learning.
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Vehicle detection through image processing
This document discusses vehicle detection using image processing. It describes how sensors can detect vehicles using transducers to detect their presence and convert the output into electrical signals. Sensors are either in-roadway, requiring installation in the road, or over-roadway, mounted above the road. The document focuses on detecting vehicles using image and video processing by extracting the vehicle portion from images in both the spatial and frequency domains, and matching the vehicle's aspect ratio to detect its type. It proposes applications for automated traffic management, toll collection, and security.
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Vehicle accident detection and messaging system using GSM and arduino
The document describes a vehicle accident detection system that uses piezoelectric sensors to detect vibrations during an accident. When an accident is detected, an Arduino microcontroller connected to the sensors triggers a GSM module to send notification messages to authorized users. It also triggers a buzzer to alert people nearby. The system is intended to help reduce the number of deaths from road accidents.
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GitHub Link: https://github.com/shahsamkit73/Auto-Assistance-System-for-visually-impaired
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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.
Wheel chair controller using eye
This document describes the design and implementation of an eye movement controlled wheelchair. It uses a webcam to capture eye movements which are then processed using MATLAB to detect the direction of movement. Based on the detected movement, MATLAB sends a signal over a serial port to a microcontroller which controls the motors to move the wheelchair in the intended direction. The system was tested for accuracy and safety features were incorporated like speed control and halt functions. While the performance was around 70-90% accuracy, further work is needed to improve reliability for commercial use.
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.
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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.
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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.
ACCIDENT DETECTION AND VEHICLE TRACKING USING GPS,GSM AND MEMS
This document describes a vehicle accident detection and tracking system using GPS, GSM, and MEMS sensors. The system detects accidents using a MEMS sensor and then uses GPS to determine the vehicle's location. The location is sent via GSM to emergency services and authorized contacts to provide rapid response. The system aims to quickly locate accident sites and notify help in remote areas with limited communication infrastructure.
SMART TRAFFIC CONTROL
The project is designed to develop a density based dynamic traffic signal system having remote override facilities. During normal time the signal timing changes automatically on sensing the traffic density at the junction but in the event of any emergency vehicle like ambulance, fire brigade etc. requiring priority are built in with RF remote control to override the set timing by providing instantaneous green signal in the desired direction while blocking the other lanes by red signal for some time. Traffic congestion is a severe problem in many major cities across the world thus it is felt imperative to provide such facilities to important vehicles.
Conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density. Junction timings allotted are fixed. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time. The proposed system using a PIC microcontroller duly interfaced with sensors, changes the junction timing automatically to accommodate movement of vehicles smoothly avoiding unnecessary waiting time at the junction. The sensors used in this project are IR, are in line of sight configuration across the loads to detect the density at the traffic signal. The override feature is activated by an on board RF transmitter operated from the emergency vehicle.
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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.
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Today, every person facing the problem of traffic jam on the road, road accident is the measure problem for us. So this device “SMART VEHICLE SECURA”, which can be useful for stopping the accident and also useful for saving the life of human being. It is a device that works to provide the security of the vehicles.
Hand Gesture Recognition
Human Computer Interaction, Gesture provides a way for computers to understand human body language, Deals with the goal of interpreting hand gestures via mathematical algorithms, Enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices
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This document describes the implementation of a smart helmet system using solar power. The system includes an alcohol sensor, temperature sensor, vibration sensor, GPS module, GSM module, and microcontroller to provide safety features like detecting accidents, monitoring alcohol levels, tracking location, and regulating temperature. The smart helmet aims to reduce accidents by preventing intoxicated riding and automatically alerting emergency contacts in the event of a crash. It can also be used to remotely immobilize a vehicle if the helmet is removed or stolen. The system design uses low power components like a solar cell to allow for portable operation.
Driver drowsiness monitoring system using visual behavior and Machine Learning.
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Vehicle detection through image processing
This document discusses vehicle detection using image processing. It describes how sensors can detect vehicles using transducers to detect their presence and convert the output into electrical signals. Sensors are either in-roadway, requiring installation in the road, or over-roadway, mounted above the road. The document focuses on detecting vehicles using image and video processing by extracting the vehicle portion from images in both the spatial and frequency domains, and matching the vehicle's aspect ratio to detect its type. It proposes applications for automated traffic management, toll collection, and security.
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.
Vehicle accident detection system (VAD)
The main objective of our project is to provide an optimum solution to the traffic hazards and the road accidents. According to this project when a vehicle meets with an accident, immediately vibration sensor will detect the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM MODEM including the location to police control room or a rescue team. So the police can immediately trace the location through the GPS MODEM after receiving the information.
ACCIDENT PREVENTION AND DETECTION SYSTEM
This document describes a student project to develop an Accident Prevention and Detection System (APDS) mobile application. The application uses GPS to track vehicle speed and location. If the speed exceeds a preset limit, the app will sound an alarm. If the user does not respond, emergency contacts will be notified. It will also detect sudden drops in speed and alert nearby hospitals. The goal is to reduce response times and save lives in the event of a vehicular accident. A group of students at K. K. Wagh Polytechnic are developing the Android app under faculty guidance with sponsorship from Sumago Infotech.
Face recognition attendance system
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes.
Vehicle accident detection and messaging system using GSM and arduino
The document describes a vehicle accident detection system that uses piezoelectric sensors to detect vibrations during an accident. When an accident is detected, an Arduino microcontroller connected to the sensors triggers a GSM module to send notification messages to authorized users. It also triggers a buzzer to alert people nearby. The system is intended to help reduce the number of deaths from road accidents.
auto-assistance system for visually impaired person
The World Health Organization (WHO) reported that there are 285 million visually-impaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. One of the most difficult activities that must be conducted by visually impaired is indoor navigation. In indoor environment, visually impaired should be aware of obstacles in front of them and be able to avoid it. The use of powered wheelchairs with high transportability and obstacle avoidance intelligence is one of the great steps towards the integration of physically disabled and mentally handicapped people. The disable person will not be able to visualize the object so this Auto-assistance system may suffice the requirement. Auto-Assistance System operating in dynamic environments need to sense its surrounding environment and adapt the control signal in real time to avoid collisions and protect the users. Auto-Assistance System that assist or replace user control could be developed to serve for these users, utilizing systems and algorithms from Auto-Assistance robots. This system could be used to assist disable in their mobility by warning of obstacles. The system could be used in indoor environment like hospital, public garden area. So, we are designing an Auto-assistance system which will help the visually impaired person to work independently. In this system we would be detecting the obstruction in the path of visually impaired person using USB Camera & help them to avoid the collisions.
GitHub Link: https://github.com/shahsamkit73/Auto-Assistance-System-for-visually-impaired
Computer vision
Computer vision is the goal of writing programs that can interpret images, such as video sequences or medical scans. It involves acquiring images, preprocessing them, extracting features, detecting/segmenting objects, and recognizing/interpreting the images. Computer vision draws from fields like calculus, linear algebra, and statistics. It has applications in areas like robotics, navigation, inspection, and medical imaging. While computer vision has improved, it still lacks the subtlety and versatility of human vision.
QSpiders - Aptitude Assignments
Interest Assignments
Partnership Assignments
Percentages Assignment
Profit and Loss
Assignments
Proportion Assignments
Set Theory Assignments
Time and Distance Assignments
Time and Work Assignments
Permutation Assignments
Allegation Assignments
AP,GP Assignments
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.
Wheel chair controller using eye
This document describes the design and implementation of an eye movement controlled wheelchair. It uses a webcam to capture eye movements which are then processed using MATLAB to detect the direction of movement. Based on the detected movement, MATLAB sends a signal over a serial port to a microcontroller which controls the motors to move the wheelchair in the intended direction. The system was tested for accuracy and safety features were incorporated like speed control and halt functions. While the performance was around 70-90% accuracy, further work is needed to improve reliability for commercial use.
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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.
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This document describes a vehicle accident prevention system using an eye blink sensor. The system uses an IR sensor to detect a driver's eye blinks and a microcontroller to process the sensor data. If no eye blinks are detected for a period of time, indicating potential drowsiness, the system will stop the vehicle and trigger an alarm to prevent accidents. The system could also be expanded in the future to detect alcohol and stop the vehicle if the driver is intoxicated.
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This document describes a project to prevent vehicle accidents by detecting a driver's eye blinks using an IR sensor. If the sensor detects no eye blinks for a set time, indicating potential drowsiness, it will send a signal via RF to receivers in the vehicle to stop the motor and alert emergency services. The system uses a microcontroller, eye blink sensor, RF transmitter and receiver, motor driver, and other components. It aims to reduce accidents caused by drowsy or intoxicated driving. Possible extensions include adding alcohol detection to also stop the vehicle if the driver is intoxicated.
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.5 drowsy-detector
The document describes the design of a Drowsy Driver Detection System. The system uses a camera to monitor the driver's face and eyes in order to detect fatigue. It analyzes the video input to locate the eyes and determine if they are open or closed. If the eyes are closed for 5 consecutive frames, the system concludes the driver is falling asleep and issues a warning signal. The system is designed to work in both day and night conditions using a machine vision based approach with the goal of developing a non-intrusive real-time fatigue monitoring system.
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.
Session 48 Johan Karlsson
The document discusses a research project that aims to improve driver drowsiness and distraction detection through sensor fusion. It describes collecting data from drivers on test routes to train and optimize a classifier. Multiple indicators of drowsiness are measured, like blink duration and lane keeping variability. A support vector machine is used to fuse the sensor data and find the optimal detection threshold. The goal is to enhance overall detection performance by combining data from different sensors and reducing false alerts. Evaluation involves calculating the sensitivity and specificity of detection compared to a ground truth scale.
Accident prevention using bluetooth tech and eye blink sensor
detailed description of accident prevention using bluetooth technology and eye blink sensor
get benifited
Car safety system
This document describes a smart car safety system that uses IoT and AI concepts to detect accidents, drowsiness, and alcohol levels. It has two main functions: distance detection using ultrasonic sensors to detect nearby objects and alert the driver, and eye blink detection using a camera and image processing to monitor for drowsiness. If an accident is detected by impact sensors, the system sends an SMS with location and other details using GSM. Its goals are to prevent accidents from drowsiness and drunk driving by monitoring the driver and vehicle.
Accident prevention using wireless communication
This document proposes using wireless communication via Bluetooth to prevent vehicle accidents. It discusses how accidents are often caused by human error and high speeds. A Bluetooth system would allow up to eight nearby vehicles to communicate their speeds and automatically apply brakes if vehicles get too close. The system would use sensors to monitor distance, a pump to control hydraulic brake pressure, and valves to increase pressure and braking force when needed to prevent collisions. Overall, the document argues that Bluetooth-enabled automatic braking could help reduce accidents by addressing human errors related to speed and distance between vehicles.
Car speed control using bluetooth
This document discusses using Bluetooth technology to automatically control car speeds and implement emergency braking to prevent accidents. It describes how Bluetooth can communicate with up to 8 devices within 100 meters to monitor car speeds. When two cars come within 10 meters, an automatic braking system engages that uses hydraulic valves and pressure to increase braking force and slow the vehicles. The system aims to reduce accidents caused by human error and high speeds.
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
Pelton_Presentation_Drowsy_Driving_PCoughlin_DRubenstein_KSingh
The document describes a drowsy driving detection and alert system that was created to help reduce accidents caused by drowsy driving. The system uses sensors on the steering wheel, seat belt, and dashboard camera to monitor the driver for symptoms of drowsiness like reduced grip on the steering wheel, declining breath rate, and prolonged eye closure. It then analyzes the sensor data and alerts the driver if it determines they are drowsy. Testing showed the individual subsystems worked correctly but the full system failed in low light and when the driver wore glasses. Future work includes integrating the system into vehicles and further testing to determine conditions that cause drowsiness.
chapter 8
This document discusses machine learning techniques for detecting driver drowsiness using facial analysis. Facial movements of subjects playing a driving video game were analyzed using classifiers trained on the Facial Action Coding System to detect 31 facial actions. Head motion was also tracked. Machine learning classifiers like Adaboost and logistic regression were able to predict sleep or crash episodes with 98% accuracy based on patterns of facial actions like blinking, yawning and head movements. New facial behaviors associated with drowsiness were revealed through this automated facial analysis approach.
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.
Automatic vehicle accident detection and messaging system using gsm and gps m...
This document summarizes an automatic vehicle accident detection and messaging system using GSM and GPS technology. The system uses a microcontroller, GSM modem, and GPS modem to detect if an accident occurs and send an SMS message with the vehicle's location to alert contacts. Some key advantages are providing security while traveling by detecting accident locations and notifying others. The system could also be expanded to track stolen vehicles or interface with other vehicle security systems.
Cat fatigue technology report 2008
Caterpillar conducted a review of 22 fatigue detection technologies for use in the mining industry. They developed an objective assessment matrix to score the technologies based on 16 categories and 93 total features, with weightings from mining customers and fatigue experts. The top technologies identified were ASTiDTM, FaceLab, HaulCheck, OptalertTM, and the Driver State Monitor. Simulator trials showed promise for FaceLab and Driver State Monitor, but more development is needed. Head nod sensors performed poorly in trials. Overall, ASTiDTM and OptalertTM were recommended as viable options to support fatigue management programs, but technology should not replace good policies and taking responsibility for fitness for work.
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This document describes a drowsiness detection model built using Python. The model uses computer vision and a pre-trained facial landmark detection model to identify a driver's eyes in video from a webcam. It calculates the eye aspect ratio over time to determine if the driver's eyes are closed, indicating drowsiness. If drowsiness is detected by the eyes being closed for a certain period, an alarm sound is triggered using pygame to alert the driver. The goal is to create an affordable and effective drowsiness detection system to help reduce accidents caused by tired drivers. The model was tested and successfully detected open and closed eyes and triggered alarms appropriately.
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Prototyping presentation
Group members working on the project include Amara Jamal Ibtihaj Uddin Jaweria Nadeem Khan. The supervisor is Farhan Ahmed Siddiqui. The document discusses an overview of computer vision and object detection in images, with a focus on using these techniques for automatic license plate recognition. It describes the objectives, applications, logical structure, cycle diagram, problems, hardware design, and results of their system for license plate detection and monitoring of vehicles entering a parking area.
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The document describes a real-time drowsiness detection system that uses a webcam to monitor a driver's face. It detects drowsiness by analyzing features like eye aspect ratio (EAR) and mouth aspect ratio (MAR) over time. The system first locates the face, then eyes and mouth. It calculates EAR by measuring eye closure distances and MAR by measuring mouth opening distances. If EAR and MAR fall below thresholds for a certain number of frames, an alert is triggered by displaying text, sound or sending an SMS message to a designated emergency number. The system was tested over 20 days on people with and without things like glasses or facial hair. It provides a non-intrusive way to detect drowsiness
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Driver drowsiness detection
- 2. OBJECTIVE • Nowadays the driver safety in the car is one of the most wanted system to avoid accidents. Our objective of the project is to ensure the safety system. For enhancing the safety, we are detecting the eye blinks of the driver and estimating the driver status and control the car accordingly.
- 3. INTRODUCTION: • Aim of this project is implementing the system as a prototype by capturing the live images of the eyes and fed them in to the Microcontroller in which the MATLAB software is used to process the video and convert it in to frames and process it accordingly. Some customized algorithms are coded in MATLAB for image segmentation of eyes from the entire image and image recognition of the eyes and face position. • On the whole, by sensing the eye blinks we can decide if the eye blinks are more than the driver is very sleepy, Drinking and it will automatically turn off the vehicle, if the driver is showing the left eye ball position than the left indicator of the car is turned on and so on.
- 5. MATLAB WITH MICROCONTRILLER 16FXXX Image Processor Camera E C U Arduino Hardware Power Supply
- 6. METHODOLOGY: • The eye blinks of the driver and estimating the driver status and control the car accordingly. We are implementing the system as a prototype by capturing the live images of the eyes and fed them in to the Microcontroller in which the MATLAB software is used to process the video and convert it in to frames and process it accordingly. Some customized algorithms are coded in MATLAB for image segmentation of eyes from the entire image and image recognition of the eyes and face position. On the whole, by sensing the eye blinks we can decide if the eye blinks are more than the driver is very sleepy, Drinking and it will automatically turn off the vehicle, if the driver is showing the left eye ball position than the left indicator of the car is turned on and so on. Y - - Z - - -
- 10. Future’s Anti-drowsiness Alarm Make Easy drive Drink and Drive production Medical accent Directly interface to any Hardware using Arduino Low cost High Security This program can use for Aircraft Also
- 11. Camera
- 12. Image Captured by Camera
- 13. Programming Environment (soft-&Hardware) For test and first implementations Matlad was used. It is not as fast as C code id , but not fast enough for real time experiences. Another Avanade of Matlab on Windows machines is the possibility to simple access webcams with the image acquisition . On the hardware side image processor Logitech quickcam pro model we used .
- 14. Image Processing A lot of way can be developed to find pupils in the given area surrounding the eyes. It can also be done using hue or saturation, which leads controlled conditions given to good results , but it highly depends on the current light situation. Thus, another way is used to find the pupils. A picture of the pupil runs over the current picture area and tries to find the place with the highest accordance. Different pupils where used for testing and the best result were gained by pupils directly from the tester, which was not really surprising. Obtaining them is not that simple that simple thought. We name this algorithm is called Eagle Eye Safety. Which requires too much calculating time to be used in real time environment's, but is fact enough for getting pupils
- 15. o Microchip's 16-bit, PIC24 MCUs and dsPIC® Digital Signal Controllers provide designers with an easy upgrade path from 8-bit PIC® microcontrollers and a cost effective option to 32-bit MCUs. The broad product line includes everything from eXtreme Low Power microcontrollers to high performance digital signal controllers. With single cycle execution, deterministic interrupt response, zero overhead looping, and fast DMA, the dsPIC family also adds a single cycle 16x16 MAC and 40-bit accumulators, ideal for math intensive applications like motor control and digital power.
- 16. Arduino hardware MATLAB Support Package for Arduino hardware enables you to use MATLAB to communicate with the Arduino® board over a USB cable. This package is based on a server program running on the board, which listens to commands arriving via serial port, executes the commands, and, if needed, returns a result. This approach helps you
- 17. E C U
Editor's Notes
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