781 research outputs found
Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
This work presents the development of an ADAS (advanced driving assistance system)
focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state
to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is
performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is
not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are
recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms
of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false
positives. The first alternative uses a recurrent and convolutional neural network, while the second
one uses deep learning techniques to extract numeric features from images, which are introduced into
a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65%
accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system
stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in
which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do
not achieve very satisfactory rates, the proposals presented in this work are promising and can be
considered a solid baseline for future works.This work was supported by the Spanish Government under projects PID2019-
104793RB-C31, TRA2016-78886-C3-1-R, RTI2018-096036-B-C22, PEAVAUTO-CM-UC3M and by the
Region of Madrid’s Excellence Program (EPUC3M17)
Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers
Drowsiness on the road is a widespread problem with fatal consequences; thus,
a multitude of systems and techniques have been proposed. Among existing
methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early
signs of drowsiness, but their algorithm was tested only on a powerful desktop
computer, which is not practical to apply in a moving vehicle setting. In this
paper, we propose an efficient platform to run Ghoddosian's algorithm, detail
the performance tests we ran to determine this platform, and explain our
threshold optimization logic. After considering the Jetson Nano and Beelink
(Mini PC), we concluded that the Mini PC is the most efficient and practical to
run our embedded system in a vehicle. To determine this, we ran communication
speed tests and evaluated total processing times for inference operations.
Based on our experiments, the average total processing time to run the
drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the
Beelink (Mini PC). Considering the portability and power efficiency of each
device, along with the processing time results, the Beelink (Mini PC) was
determined to be most suitable. Also, we propose a threshold optimization
algorithm, which determines whether the driver is drowsy or alert based on the
trade-off between the sensitivity and specificity of the drowsiness detection
model. Our study will serve as a crucial next step for drowsiness detection
research and its application in vehicles. Through our experiment, we have
determinend a favorable platform that can run drowsiness detection algorithms
in real-time and can be used as a foundation to further advance drowsiness
detection research. In doing so, we have bridged the gap between an existing
embedded system and its actual implementation in vehicles to bring drowsiness
technology a step closer to prevalent real-life implementation.Comment: 26 pages, 13 figures, 4 table
How Facial Features Convey Attention in Stationary Environments
Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open-source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified the Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNNs and CRNNs). Interestingly, CRNNs did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods
Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review
Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships
Modern drowsiness detection techniques: a review
According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness
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