Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-to-deploy real-world systems to detect the onset of drowsiness.In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.
As pointed out above, there are numerous works in drowsiness detection, but none of them uses a dataset that is both public and realistic. As a result, it is difficult to compare prior methods to each other and to decide what the state of the art is in this area. Several existing methods @cite_6 @cite_19 @cite_24 @cite_2 @cite_16 were evaluated on a small number of subjects without sharing the videos. In some cases @cite_3 @cite_15 the subjects were instructed to act drowsy, as opposed to obtaining data from subjects who were really drowsy.
@cite_6 @cite_3 @cite_24 @cite_19 @cite_2 @cite_15 @cite_16
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Nowadays, driving support systems, such as car navigation systems, are getting common, and they support drivers in several aspects. It is important for driving support systems to detect status of driver's consciousness. Particularly, detecting driver's drowsiness could prevent drivers from collisions caused by drowsy driving. In this paper, we discuss a system and a method for detecting driver's drowsiness, which use a property of blinks waveforms that driver's eye-closing time is strongly connected to their drowsiness. Moreover, our system detects the drowsiness robustly for individual differences by using three factors extracted from the individual blink waveform. Research has shown that sustained attention or vigilance declines over time on task. Sustained attention is necessary in many environments such as air traffic controllers, cyber operators, and imagery analysts. A lapse of attention in any one of these environments can have harmful consequences. The purpose of this study was to determine if eye blink metrics from an eye-tracker are related to changes in vigilance performance and cerebral blood flow velocities. Nineteen participants performed a vigilance task while wearing an eye-tracker on four separate days. Blink frequency and duration changed significantly over time during the task. Both blink frequency and duration increased as performance declined and right cerebral blood flow velocity declined. These results suggest that eye blink information may be an indicator of arousal levels. Using an eye-tracker to detect changes in eye blinks in an operational environment would allow preventative measures to be implemented, perhaps by providing perceptual warning signals or augmenting human cognition through non-invasive brain stimulation techniques. Language: en Drowsiness is one of the main causes of severe traffic accidents occurring in our daily life. In order to reduce the number of drowsiness-induced accidents, various researches have been conducted with the aim of finding practical and non-invasive drowsiness detection systems by using behavioral measuring techniques. Many of the previous works on behavioral measuring techniques have mainly focused on the analysis of eye closure and blinking of the driver. It is recently that more attention started to shift to inclusion of other facial expressions and only few, among those researches, have been done on the analysis of temporal dynamics of facial expressions for drowsiness detection. In this paper we propose a new method of analyzing the facial expression of the driver through Hidden Markov Model (HMM) based dynamic modeling to detect drowsiness. We have implemented the algorithm using a simulated driving setup. Experimental results verified the effectiveness of the proposed method. Driver’s status is crucial because one of the main reasons for motor vehicular accidents is related to driver’s inattention or drowsiness. Drowsiness detector on a car can reduce numerous accidents. Accidents occur because of a single moment of negligence, thus driver monitoring system which works in real-time is necessary. This detector should be deployable to an embedded device and perform at high accuracy. In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed. Main contribution of our paper is compression of heavy baseline model to a light weight model deployable to an embedded board. Moreover, minimized network structure was designed based on facial landmark input to recognize whether driver is drowsy or not. The proposed model achieved an accuracy of 89.5 on 3-class classification and speed of 14.9 frames per second (FPS) on Jetson TK1. Fatigue during long-time driving threatens the safety of drivers and transportation. In this paper, we provide an effective method based on multi-sensor signals collected from Kinect2.0 camera and PPG pulse sensor to build a driver fatigue detection system. Unlike most traditional works, we define the transitional process of fatigue and elaborate its effect on training classifiers. The simulation experiments are then designed and 15 groups of data are collected. Our method works in the following steps: 1) feature extraction and fusion, 2) sample labelling and 3) SVM classifier designing. The 10-fold cross-validation accuracy of the classifier is 90.10 and the test accuracy is 83.82 . Experimental results verify that our method to deal with samples in transitional process is universal and more accurate than traditional methods. Moreover, our method based on multi-sensor works better than those dealing with single-sensor.