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
Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the cl...Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.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
A handwriting input system was developed using three collinear ultrasonic transducers. These collinear polyvinylidene fluoride （PVDF） transducers were specially designed for the handwriting input system to give a la...A handwriting input system was developed using three collinear ultrasonic transducers. These collinear polyvinylidene fluoride （PVDF） transducers were specially designed for the handwriting input system to give a large writeable area with writing in any direction. Driver and detection circuits were developed for the handwriting system. This handwriting input system based on 2-dimensional position tracing has large writeable area （A4 paper）, low drive voltage （5 V）, and is independent of the handwriting pad or the pen.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The work of this paper was supported by the National Natural Science Foundation of China under grant numbers 61572038 received by J.Z.in 2015.URL:https://isisn.nsfc.gov.cn/egrantindex/funcindex/prjsearch-list。
文摘Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.
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.
基金Supported by the National Natural Science Foundation of China(Nos. 61025021,60936002, 60729308,and 61020106006)the National High-Tech Research and Development (863) Program of China (No.2006AA04Z372)the National Key Projects of Science and Technology of China (No.2009ZX02023-1-3)
文摘A handwriting input system was developed using three collinear ultrasonic transducers. These collinear polyvinylidene fluoride （PVDF） transducers were specially designed for the handwriting input system to give a large writeable area with writing in any direction. Driver and detection circuits were developed for the handwriting system. This handwriting input system based on 2-dimensional position tracing has large writeable area （A4 paper）, low drive voltage （5 V）, and is independent of the handwriting pad or the pen.
