Submit Search
Upload
DRIVER DROWSINESS DETECTION SYSTEMS
•
0 likes
•
24 views
IRJET Journal
Follow
https://www.irjet.net/archives/V10/i2/IRJET-V10I223.pdf
Read less
Read more
Engineering
Report
Share
Report
Share
1 of 4
Download now
Download to read offline
Recommended
MACHINE LEARNING BASED DRIVER MONITORING SYSTEM
MACHINE LEARNING BASED DRIVER MONITORING SYSTEM
IRJET Journal
DRIVER DROWSINESS DETECTION USING DEEP LEARNING
DRIVER DROWSINESS DETECTION USING DEEP LEARNING
IRJET Journal
A New Proposal for Smartphone Based Drowsiness Detection and Warning System f...
A New Proposal for Smartphone Based Drowsiness Detection and Warning System f...
YogeshIJTSRD
A Virtual Reality Based Driving System
A Virtual Reality Based Driving System
Associate Professor in VSB Coimbatore
Driver_drowsiness_Detection_and_Alarming_System_Using_Machine_Learning.pptx
Driver_drowsiness_Detection_and_Alarming_System_Using_Machine_Learning.pptx
meghnadutt
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
IRJET Journal
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
IRJET Journal
Ijetcas14 395
Ijetcas14 395
Iasir Journals
Recommended
MACHINE LEARNING BASED DRIVER MONITORING SYSTEM
MACHINE LEARNING BASED DRIVER MONITORING SYSTEM
IRJET Journal
DRIVER DROWSINESS DETECTION USING DEEP LEARNING
DRIVER DROWSINESS DETECTION USING DEEP LEARNING
IRJET Journal
A New Proposal for Smartphone Based Drowsiness Detection and Warning System f...
A New Proposal for Smartphone Based Drowsiness Detection and Warning System f...
YogeshIJTSRD
A Virtual Reality Based Driving System
A Virtual Reality Based Driving System
Associate Professor in VSB Coimbatore
Driver_drowsiness_Detection_and_Alarming_System_Using_Machine_Learning.pptx
Driver_drowsiness_Detection_and_Alarming_System_Using_Machine_Learning.pptx
meghnadutt
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
IRJET Journal
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
Driver Dormant Monitoring System to Avert Fatal Accidents Using Image Processing
IRJET Journal
Ijetcas14 395
Ijetcas14 395
Iasir Journals
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
IRJET Journal
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
IRJET Journal
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
IRJET Journal
PBL-2 pptx.pdf
PBL-2 pptx.pdf
PrajwalGaikwad32
Real-Time Driver Drowsiness Detection System
Real-Time Driver Drowsiness Detection System
IRJET Journal
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
NIET Journal of Engineering & Technology (NIETJET)
DROWSINESS DETECTION USING COMPUTER VISION
DROWSINESS DETECTION USING COMPUTER VISION
IRJET Journal
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
IRJET Journal
DRIVER DROWSINESS DETECTION SYSTEM
DRIVER DROWSINESS DETECTION SYSTEM
IRJET Journal
IRJET- Self Driving Car using Deep Q-Learning
IRJET- Self Driving Car using Deep Q-Learning
IRJET Journal
Driver Drowsiness Detection report
Driver Drowsiness Detection report
PurvanshJain1
IRJET- Drowziness Detection with Alarm Monitoring
IRJET- Drowziness Detection with Alarm Monitoring
IRJET Journal
DDD_ML.docx
DDD_ML.docx
AnanthavenkateshGumm1
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
IRJET Journal
In advance accident alert system & Driver Drowsiness Detection
In advance accident alert system & Driver Drowsiness Detection
IRJET Journal
DROWSINESS DETECTION MODEL USING PYTHON
DROWSINESS DETECTION MODEL USING PYTHON
IRJET Journal
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
IRJET Journal
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
IRJET Journal
Driver detection system_final.ppt
Driver detection system_final.ppt
MaseeraAhmed1
Driver Drowsiness Detection System Using Image Processing
Driver Drowsiness Detection System Using Image Processing
IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
More Related Content
Similar to DRIVER DROWSINESS DETECTION SYSTEMS
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
IRJET Journal
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
IRJET Journal
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
IRJET Journal
PBL-2 pptx.pdf
PBL-2 pptx.pdf
PrajwalGaikwad32
Real-Time Driver Drowsiness Detection System
Real-Time Driver Drowsiness Detection System
IRJET Journal
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
NIET Journal of Engineering & Technology (NIETJET)
DROWSINESS DETECTION USING COMPUTER VISION
DROWSINESS DETECTION USING COMPUTER VISION
IRJET Journal
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
IRJET Journal
DRIVER DROWSINESS DETECTION SYSTEM
DRIVER DROWSINESS DETECTION SYSTEM
IRJET Journal
IRJET- Self Driving Car using Deep Q-Learning
IRJET- Self Driving Car using Deep Q-Learning
IRJET Journal
Driver Drowsiness Detection report
Driver Drowsiness Detection report
PurvanshJain1
IRJET- Drowziness Detection with Alarm Monitoring
IRJET- Drowziness Detection with Alarm Monitoring
IRJET Journal
DDD_ML.docx
DDD_ML.docx
AnanthavenkateshGumm1
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
IRJET Journal
In advance accident alert system & Driver Drowsiness Detection
In advance accident alert system & Driver Drowsiness Detection
IRJET Journal
DROWSINESS DETECTION MODEL USING PYTHON
DROWSINESS DETECTION MODEL USING PYTHON
IRJET Journal
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
IRJET Journal
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
IRJET Journal
Driver detection system_final.ppt
Driver detection system_final.ppt
MaseeraAhmed1
Driver Drowsiness Detection System Using Image Processing
Driver Drowsiness Detection System Using Image Processing
IRJET Journal
Similar to DRIVER DROWSINESS DETECTION SYSTEMS
(20)
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
An Eye State Recognition System using Transfer Learning: Inception‑Based Deep...
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
IRJET- Vehicular Data Acquisition & Driver Behaviours Surveillance System
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
Automated Framework for Vision based Driver Fatigue Detection by using Multi-...
PBL-2 pptx.pdf
PBL-2 pptx.pdf
Real-Time Driver Drowsiness Detection System
Real-Time Driver Drowsiness Detection System
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
DROWSINESS DETECTION SYSTEM USING MACHINE LEARNING
DROWSINESS DETECTION USING COMPUTER VISION
DROWSINESS DETECTION USING COMPUTER VISION
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking...
DRIVER DROWSINESS DETECTION SYSTEM
DRIVER DROWSINESS DETECTION SYSTEM
IRJET- Self Driving Car using Deep Q-Learning
IRJET- Self Driving Car using Deep Q-Learning
Driver Drowsiness Detection report
Driver Drowsiness Detection report
IRJET- Drowziness Detection with Alarm Monitoring
IRJET- Drowziness Detection with Alarm Monitoring
DDD_ML.docx
DDD_ML.docx
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
IRJET- Igaze-Eye Gaze Direction Evaluation to Operate a Virtual Keyboard for ...
In advance accident alert system & Driver Drowsiness Detection
In advance accident alert system & Driver Drowsiness Detection
DROWSINESS DETECTION MODEL USING PYTHON
DROWSINESS DETECTION MODEL USING PYTHON
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
Facial-Expression Based Mouse Cursor Control for Physically Challenged Indivi...
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
IRJET- Analysis of Yawning Behavior in IoT based of Drowsy Drivers
Driver detection system_final.ppt
Driver detection system_final.ppt
Driver Drowsiness Detection System Using Image Processing
Driver Drowsiness Detection System Using Image Processing
More from IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
More from IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
React based fullstack edtech web application
React based fullstack edtech web application
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Recently uploaded
Glass Ceramics: Processing and Properties
Glass Ceramics: Processing and Properties
Prabhanshu Chaturvedi
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghly
sanyuktamishra911
(ANJALI) Dange Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANJALI) Dange Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
ranjana rawat
(PRIYA) Rajgurunagar Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(PRIYA) Rajgurunagar Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
ranjana rawat
The Most Attractive Pune Call Girls Budhwar Peth 8250192130 Will You Miss Thi...
The Most Attractive Pune Call Girls Budhwar Peth 8250192130 Will You Miss Thi...
ranjana rawat
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
Call Girls in Nagpur High Profile
UNIT-II FMM-Flow Through Circular Conduits
UNIT-II FMM-Flow Through Circular Conduits
rknatarajan
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
dharasingh5698
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Call Girls in Nagpur High Profile
The Most Attractive Pune Call Girls Manchar 8250192130 Will You Miss This Cha...
The Most Attractive Pune Call Girls Manchar 8250192130 Will You Miss This Cha...
ranjana rawat
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
roncy bisnoi
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Asst.prof M.Gokilavani
University management System project report..pdf
University management System project report..pdf
Kamal Acharya
Booking open Available Pune Call Girls Koregaon Park 6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Koregaon Park 6297143586 Call Hot Ind...
Call Girls in Nagpur High Profile
BSides Seattle 2024 - Stopping Ethan Hunt From Taking Your Data.pptx
BSides Seattle 2024 - Stopping Ethan Hunt From Taking Your Data.pptx
fenichawla
Call Girls in Nagpur Suman Call 7001035870 Meet With Nagpur Escorts
Call Girls in Nagpur Suman Call 7001035870 Meet With Nagpur Escorts
Call Girls in Nagpur High Profile
Top Rated Pune Call Girls Budhwar Peth ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Top Rated Pune Call Girls Budhwar Peth ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Call Girls in Nagpur High Profile
Roadmap to Membership of RICS - Pathways and Routes
Roadmap to Membership of RICS - Pathways and Routes
M Maged Hegazy, LLM, MBA, CCP, P3O
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
slot gacor bisa pakai pulsa
UNIT-V FMM.HYDRAULIC TURBINE - Construction and working
UNIT-V FMM.HYDRAULIC TURBINE - Construction and working
rknatarajan
Recently uploaded
(20)
Glass Ceramics: Processing and Properties
Glass Ceramics: Processing and Properties
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghly
(ANJALI) Dange Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANJALI) Dange Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(PRIYA) Rajgurunagar Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(PRIYA) Rajgurunagar Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
The Most Attractive Pune Call Girls Budhwar Peth 8250192130 Will You Miss Thi...
The Most Attractive Pune Call Girls Budhwar Peth 8250192130 Will You Miss Thi...
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
UNIT-II FMM-Flow Through Circular Conduits
UNIT-II FMM-Flow Through Circular Conduits
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
Booking open Available Pune Call Girls Pargaon 6297143586 Call Hot Indian Gi...
The Most Attractive Pune Call Girls Manchar 8250192130 Will You Miss This Cha...
The Most Attractive Pune Call Girls Manchar 8250192130 Will You Miss This Cha...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
University management System project report..pdf
University management System project report..pdf
Booking open Available Pune Call Girls Koregaon Park 6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Koregaon Park 6297143586 Call Hot Ind...
BSides Seattle 2024 - Stopping Ethan Hunt From Taking Your Data.pptx
BSides Seattle 2024 - Stopping Ethan Hunt From Taking Your Data.pptx
Call Girls in Nagpur Suman Call 7001035870 Meet With Nagpur Escorts
Call Girls in Nagpur Suman Call 7001035870 Meet With Nagpur Escorts
Top Rated Pune Call Girls Budhwar Peth ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Top Rated Pune Call Girls Budhwar Peth ⟟ 6297143586 ⟟ Call Me For Genuine Se...
Roadmap to Membership of RICS - Pathways and Routes
Roadmap to Membership of RICS - Pathways and Routes
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
UNIT-V FMM.HYDRAULIC TURBINE - Construction and working
UNIT-V FMM.HYDRAULIC TURBINE - Construction and working
DRIVER DROWSINESS DETECTION SYSTEMS
1.
Volume: 10 Issue:
02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 152 DRIVER DROWSINESS DETECTION SYSTEMS Supreetha Ganesh, Amit K B, Akif Delvi, C N Shreyas, Gagandeep K Supreetha Ganesh, Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India Akif Delvi, Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India Amit K B , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India C N Shreyas , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India Gagandeep K , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Drowsiness has significant contribution to the accidents on road. Accurate measurement is required to track the state of the driver. It has various shortcomings. Convolutional neural networks(CNN) developed using Keras were utilized to create the model that we employed. CNN is a branch of deep neural networks that is appropriate for image classification. It consists of many layers that include input, output and hidden layers. The drowsiness detection systems for drivers have the potential to greatly increasetrafficsafety by warning drivers to stop or take breaks when they are in danger of nodding off behind the wheel. Key words: CNN (Convolutional NeuralNetworks),deep neuralnetwork, drowsiness, python, jupyter. 1. INTRODUCTION A system called driver drowsiness detection can tell when a driver is getting fatigued or dozing off while operating a vehicle. Drowsy driving has been linked to fatal accidents and other traffic fatalities, thus this can be a serious safety risk. Driver drowsiness can be identified using a variety of methods, including: 1. Eye tracking: Some systems track the driver's eyes using eye tracking technology.Thedrivercanbenoddingoffiftheir eyes are closed or moving erratically. 2.Face-recognition technology: Some systems examine the driver's facialexpressionstolookfortirednessindicators like drooping eyelids or a slack jaw. 3. Monitoring of the driver'sheartrate:Somesystemsutilize sensors to track the driver's heart rate and alarm them if it drops below a predetermined level, which could be a sign that they are nodding off. 4. Vehicle monitoring: Some systems keepaneyeonhow the car is driving and search for indications that the driver may not be fully awake, such as lane wandering or abrupt changes in speed. 2. RELATED WORKS LSTM-CNN Architecture for Human ActivityRecognition The paper [1] proposes a model for the traditional pattern recognition techniqueshave advanced significantly in recent years. The use of deep learningtechnologies to understand human activitiesinmobileandwearablecomputingscenarios has drawn a lot ofinterest due to its growing acceptance and success. A deep neural network with Convolutional layers and long short-term memory (LSTM) was suggested in this research. With just a few model parameters,thismodelcould automaticallyextract activity featuresandcategorizethem.In conclusion, the LSTM-CNN model consistently outperforms those suggested in other research and exhibits sound generalization. A Real-Time Driving Drowsiness Detection Algorithm With Individual Differences Consideration The paper [2] proposes a development ofadrivingsleepiness detection algorithm is crucial for enhancing traffic safety. However, the majority of them are focused on developing an all-encompassing sleepiness detection technique while ignoring the variations among individual drivers. This study suggests a real-time sleepiness detection method for drivers that takes into account their unique driving styles. Finally ,to develop a offline training module and online monitoring module in the study, taking into account the individual variations of the drivers. A specific driver- specific classifier built on SVM is trained, and while driving, the per-trained classifieris used to assess the condition of the driver's eyes. Comprehensive Drowsiness Level Detection Model Combining Multi-modal Information The paper [3] proposes a drowsiness detection model that can identify all levels ofdrowsiness, from weak to strong, is presented in this study. This method is predicated on the fundamental premise. First, it is assessed how sensitive the posture index and other indices were to different degrees of drowsiness. Then, to cover all stages of drowsiness, and develop a drowsiness detection model by combining a number of indices sensitive to both weak and strong drowsiness. After drowsiness detection, future research will concentrate on the creation of arousing and arousal- maintenance systems. Thesuccessindetectingdrowsinessat a variety of degrees, even light drowsiness, will make it possible to design interfaces thatletuserschoosestimulithat are best suited to their level of drowsiness and thesettingsin which they aredriving. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 153 Real-Time Driver-Drowsiness DetectionSystem Using Facial Features The paper [4] proposes a system the DriCare uses video images to detect drivers' signs of tirednesswithoutusageof any gadgets on human body. Additionally, based on 68 critical features, It creates a newdetectingalgorithmforface regions. Then, assessment on the drivers' condition using these facial areas. It takes the features from eyes and lips and generates a warning to driver. Based on facial key points, it defines the detection zones for the face. Due to its rapid operation, DriCare works in real-time. Real Time Driver Fatigue DetectionSystem Based On Multi_Task CNN The paper [5] proposes a model for Multi- tasking Convolutiona Neural Network (ConNN) which is suggested in this article to identify driver weariness and drowsiness.When modelling a driver's behaviour, theeyes and mouth are used. Driver wearinessis tracked through changes to these traits. In contrast to studies in the literature, the suggested Multi-task ConNN model now simultaneously incorporates mouth and eye information. Calculations of the durationand percentage of closed eyes (PERCLOS) as well as the frequency and duration of mouth and yawning sneezes are used toassess driverfatigue (FOM). Threecategories in are used in this study to categorize the driver's level of weariness. The study's ability to build afaster and more effective system with justone model rather than separately building models for two different ConNNarchitectures is one of its strongest points. Future work will add the head condition, which is just as crucial as the eye and mouth conditions, and integrate the system into an embedded system. Driver’s Drowsiness Detection The paper [6] states the advancement of technology overthe last 50 years has given drivers a lot of support by ensuring high levels of comfort and safety in their automobiles. Driver weariness is one of the many possible causes of accidents, and itwill be discussed and addressed issues in this paper. This work, will employ powerful artificial intelligence-based algorithms to identify driver exhaustion and the rate of drowsiness. It suggests a method to identify driver tiredness using artificial facial traits including eye closure, yawning, and vertical distances between the eyes and mouth. The method for driving drowsiness detection and driver rate of drowsiness is proposed in thisresearchproject.Itinfersfrom the data of 9 patients that decision tree and neural network classifiers have produced superior results than linear SVM and LDA for classifying the driver into sleepy and non- drowsy. As previously mentioned, we have defined an algorithm for the Rate of Drowsiness. Decisions could be made using previous methodologies basedoncharacteristics like eye blinks and ocular closure. It has taken into account the subject's eyes and lips as features and employed contemporary classifiers tocategorize the subject as drowsy or not .Although the presented classifiers are capable of producing results that are reasonable, there is still room for improvement in their efficiency. By examining numerous other classifiers, onecan use adrowsinessdetectionclassifier thatis more reliable. The algorithm can still be enhanced by conducting research on additional datasets to increase the rate of drowsiness detection. 3. OBJECTIVES The objective is that the driver drowsiness detection system's goal is to help reduce accidents involving both passengers and vehicles. The primary objective of driver drowsiness detection is to increase road safety by reducing accidents brought on by drowsy driving. Motor vehicle collisions involving drowsy drivers frequently result in severe injuries or fatalities. The objectives include identifying drowsiness states,popping up alerts,decreasing the crash risksandalsoenhancingthe road safety. 4. METHODOLOGY In this Python project, we'll use OpenCVto collect webcam photos and feed them into a Deep Learning model that will identify whether a person's eyes are "Open" or "Closed" based on their position. For this Python project, the strategy we'll employ is as follows: Let's now examine our algorithm's operation step by step. Step 1: Take an image as input from acamera. Input is taken in the form of an image using camera. Hence, an infinite loop is created to record every frame in order to use the webcam. Step 2:Create a region ofinterest(ROI)andidentifyfaces in the image. Since Mark the face with rectangular bound and then create the region of interest(ROI).OpenCV algorithm for detection of the object accepts grayscale imagesintheformof input, so first convert the image to grayscale in order to trace out the face in it. To identify the items, colour information is not required. To find faces, we'll use the haar cascade classifier. Step 3: Identify the eyes using ROI andprovide the information to the classifier. The classifier intakes input data of eyes from ROI, which is similar to that for finding face. Prior to detecting the eyes, a cascade classifier is set for the left and right eyes.
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 154 Step 4: Whether the eyes are open or closed will be classified by the classifier. Now classifier finds the state of eye whether closed or not. For forecasting the eye state, CNN classifier is utilized. The colour image is first converted to grayscale. We then resize the image to specified pixels in accordance with how our model was trained. Step 5:Calculate a score to find out if aperson is drowsy. Find the score to determine the drowsiness . The score indicate how long the eyes are closed. Increase the score if both eyes are closed else decrease. Fig 1 : Block Diagram of Drowsiness detection 1.CNN Architecture CNN: Convolutional neural networks . These networks may sound like an odd amalgam of biology, math, and computer science with a dash of CS, but they have been some of the most important developments in the area ofcomputervision and image processing. The multi layer perceptron (MLP) is a regularized variant of the Convolutional neural networks . They were created based on how the neurons in the visual cortex ofanimals function. a. Convolution Layers The input layer, the hidden layer, and the output layer make up the convolution layers. While in neural networks every input neuron is linked to the hidden layer below it, in CNN only a small subset of input layer neurons are linked to those in the hidden layer. The features of an input image can be extracted with the aid of convolution layers. Additionally, it is capable ofnumerous tasks including edge detection. b. Pooling Layers The major objective of the pooling layer is to extract features; by doing so, it helps to reduce the size of the representationand the parameters, making this model more efficient. It also aids in minimizing over fitting.Poolinglayers are new layers that areapplied in convolution layers. The featuremaps' dimension is decreased using it. c. Fully Connected Layers A fully connected(FC) neural networkcanbeusedtoclassify the data into distinct classes after the features have been retrieved. SVM can also be used in place of fully connected layers, although doing so results in an additional layer of complexity. Completely interconnected layers to enable training of the model. This layer contains the data that is crucial to the input, and it produces aprobability that the model is attempting toforecast. 5. APPLICATION REQUIREMENTS TensorFlow An open source library forAI&ML is calledTensorFlow.Deep neuralnetworks can make extensive use of it to concentrate on training and interference. It has a comprehensive set of tools andlibraries,enablesacademicstoincludecutting-edge technology in machine learning, and makes it simple for developers to create and deploy applications that use machine learning. Numpy: It is primarily intended for numerical computations. Additionally, the Python programming language has a package called Numpy. Multidimensional arraymetricsanda substantial numberofhigh levelmathematicaloperationsare defined using Numpy. Keras: It is the TensorFlow library's interface. It is expandable, modular, and user-friendly. It supports other widely used features such asdropout, batch normalization, and pooling. Jupyter Notebook: The basic objective of the open-source scientific computing programme Jupyter Notebook is to mix equations, visuals, and live code. It supports more than 40 programming languages. The terms Julia, Python, and R are combined to form the moniker Jupyter. While Anaconda comes preinstalled, Jupyter is primarily designed for data science and analytics applications. Data sets, such as visuals and charts, are produced by modules like Matplotlib, Plotly, or Bokeh in Anaconda. Open CV: OpenCV is an open-source library used for processing image and computer vision. It has a significant part in real-time applications, which is much needed in modern world scenario.It is used to analyze images and films to find faces, objects, and handwriting. Python has a ability to handle the OpenCV when it is integrated with libraries like NumPy. This is used to identify visual patterns and features.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 155 6. CONCLUSION A non-invasive system to localize the eyes and monitor fatigue was developed. Information about the eyes position is obtained through self-developed image processing algorithm. During the monitoring, the system is able to decide if the eyes are opened or closed. When the eyes have been closed for too long, a warning signal is issued. In addition, during monitoring, the system is able to automatically detect any eye localizingerrorthatmighthave occurred. In case of this type of error, the system is able to recover and properly localize the eyes. The following conclusions were made: Image processing achieves highly accurate and reliable detection of drowsiness. Image processing offers a non-invasive approach to detecting drowsiness without the annoyance and interference. A drowsiness detection system developed around the principle of image processing judges the drivers alertness level on the basis of continuous eye closures. REFERENCES [1]Kun Xia,Jianguang Huang And Hanyu Wang,”LSTM-CNN Architecture for Human Activity Recognition”, University Of Shanghai For Science And Technology, Shanghai 200093, China, March 20, 2020. [2]Feng You, Xiaolong Li, YunboGong, Haiwei Wang, ”AReal- Time Driving Drowsiness Detection Algorithm With Individual Differences Consideration”, School Of Civil Engineering And Transportation, South China University Of Technology,Guanzhou, 510640, China, December 10, 2019 [3]Mika Sunagawa, Shin-Ichi Shikii, Wataru Nakai, Makoto Mochizuki,“Comprehensive Drowsiness Level Detection Model Combining Multimodal Information”, Ieee Sensors Journal, Vol 20, No. 7, April 1,2020 [4]Wanghua Deng, And Ruoxue Wu,”Real- Time Driver- Drowsiness Detection System Using Facial Features”,School Of Software, Yunnan University, Kunming 650000, China, August 21, 2019 [5]Burcu Kir Savas, And Yasar Becerikli, ”Real Time Driver Fatigue Detection System Based On Multi-Task ConNN”,Computer Engineering Department, Kocaeli University, Turkey, January 3,2020.
Download now