Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepin...Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.展开更多
Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sle...Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.展开更多
Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine ...Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine deep learning and 68 face model.The proposed system assesses driver tiredness by measuring the distance between key facial landmarks,such as the distance between the eyes and the angle of the mouth,to evaluate signs of drowsiness or disengagement.Real-time video feed analysis allows for continuous monitoring of the driver’s face,enabling the system to detect behavioral cues associated with distraction,such as eye closures or changes in facial expressions.The effectiveness of this method is demonstrated through a series of experiments on a dataset of driver videos,which proves that the approach can accurately assess tiredness and distraction levels under various driving conditions.By focusing on facial landmarks,the system is computationally efficient and capable of operating in real-time,making it a practical solution for in-vehicle safety systems.This paper discusses the system’s performance,limitations,and potential for future enhancements,including integration with other in-vehicle technologies to provide comprehensive driver monitoring.展开更多
文摘Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number MoE-IF-UJ-22-4100409-1.
文摘Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.
文摘Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine deep learning and 68 face model.The proposed system assesses driver tiredness by measuring the distance between key facial landmarks,such as the distance between the eyes and the angle of the mouth,to evaluate signs of drowsiness or disengagement.Real-time video feed analysis allows for continuous monitoring of the driver’s face,enabling the system to detect behavioral cues associated with distraction,such as eye closures or changes in facial expressions.The effectiveness of this method is demonstrated through a series of experiments on a dataset of driver videos,which proves that the approach can accurately assess tiredness and distraction levels under various driving conditions.By focusing on facial landmarks,the system is computationally efficient and capable of operating in real-time,making it a practical solution for in-vehicle safety systems.This paper discusses the system’s performance,limitations,and potential for future enhancements,including integration with other in-vehicle technologies to provide comprehensive driver monitoring.
