Drowsiness detection is pivotal, especially in applications like vehicle driving, to prevent accidents caused by drivers falling asleep. The primary input for such systems is usually a video stream, typically focusing on the driver’s face to track signs of drowsiness.
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Ensuring safety through drowsiness detection is crucial, so rigorous testing and validation in real-world conditions are necessary before deployment.
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Drowsiness, face, detection system, Internet of Things, sleep, Machine Learning, Haar’s cascade classifier, Image Processing, fatigue, Raspberry Pi
A major goal of our article is to develop an intelligent processing framework to minimize the road accidents. We analyzed the behaviour of the driver in real time and identified whether the driver is in drowsiness state or not. We utilized ML based technique named Haar cascades and IoT in our research. We analyzed and alerted the driver by transmitting notifications with the help of cloud system and by spraying water if the behaviour of the driver is not stable.
Algorithm analysis, Covid, supervised learning
We constructed an actual time drowsiness identification model by considering the human’s fatigue based on body changes. We utilized data visualization to interpret the sleepy state of the person and the report is transmitted to the person via email. We employed various supervised ML methods such as LR, NB, KNN, and SVM to predict and categorize the sleepiness. As a result, KNN achieved highest outcomes in prediction and categorization process.
Feature Extraction, Feature Normalization, Neural Networks, Long Short-Term Memory Network (LSTM), K-Nearest Neighbors (KNN)
To identify the drowsiness of the person, we utilized ML based approaches in our study. Through the face identification process, we spotted the eye region to identify the drowsiness. Our approach comprises of three steps i.e identification of face, eyes and then drowsiness. We detected the face of the driver by utilizing image processing technique. We employed LSTM-KNN face identification technique. We focused the eye region through EAR approach.
Drowsiness detection, Object detection, YOLO, EAR, MAR
To forecast the driver’s fatigue state, we proposed a framework named RealD3 through the utilization of enhanced ML methods. We employed a camera related video capturing approach to capture driver’s face. To identify the landmarks and facial patterns, we utilized Mediapipe face mesh 468 and YOLO. We categorized driver’s state as drowsiness state or normal state by utilizing PERCLOS. We notified persons by considering EAR and MAR values.
5.Drowsiness and Lethargy Detection Using Machine Learning Techniques
Eye aspect ratio, Mouth aspect ratio, pupil circularity, nose length, chin length, 3D face mesh, eye closing, rapid eye blinking, yawning, head posing down, head posing up, CNN
A machine learning based approach is suggested in our research to identify the drowsiness. We extracted the relevant features from real time data by utilizing 3D Face-Mesh. We normalized every feature by considering mean and standard deviation. We developed our system by employing various ML categorization techniques. In that, CNN technique offers better end results in drowsiness identification.
MQ-3 Sensor, Pi-Camera, driver drowsiness, drunk, threshold limit, ignition
In our study, a combined model is recommended to recognize the fatigue state of the driver through the utilization of MQ-3 sensor, Pi-Camera and ML methodologies. We utilized ML based Long-Short Term Memory method to identify the driver’s state by considering face and mouth region. We detected the drunken state of the driver by using MQ-3 sensor. If the detected value is not within the limited state, an alarm notification will be produced to the driver.
Open CV, Visual monitoring, SVM
For drowsiness identification, we build a behavioral framework by employing supervised learning method. We captured the video by utilizing webcam and recognized relevant facial features such as eyes and mouth and also employed ML methods to every frame. We detected the fatigue state in terms of important values like eye aspect ratio and mouth opening ratio. We utilized OpenCV library to identify facial images and alert sound also transmitted to the driver.
Awakeness Detection, FaceMesh Algorithm, Safe Driving
An innovated framework is suggested in our study to identify the drowsiness of the driver by considering the head position at the time of driving. If we detect any abnormal changes, we alert the driver. We employed FaceMesh ML approach to detect the various positions of driver’s head. We evaluated various face positions to identify the fatigue state. As a consequence, we conclude that, our suggested framework provides efficient results than other existing researches.
Electroencephalography (EEG), Linear Discriminant Analysis (LDA), Support vector machine -RBF (SVM-rbf), Random forest (RF)
A main objective of our research is to identify the sleepiness state of the driver by recording EEG signals. To evaluate the efficiency of various ML methods such as Naive Bayes, Support Vector Machines, KNN and Random Forest, they are utilized on these EEG signals. Here, we segmented the all captured information. We preprocessed the data to extract only the important features. We state that, our proposed model identifies the drowsiness very effectively.
Our recommended model’s goal is to recognize the drowsiness and mood swings of the students. We utilized several ML methods like SVM, NN, KNN and RF to identify the mood swings. We employed OpenCV library for image processing, computer vision etc. We identified student’s face through the use of Haar cascade method and differentiate the mood swings by considering the eyeball movement. Results show that, NN achieved greater efficiency.
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