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.
The DROZY dataset @cite_8 , contains multiple types of drowsiness-related data including signals such as EEG, EOG and near-infrared (NIR) images. An advantage of the DROZY dataset is that drowsiness data are obtained by subjects who are really drowsy, as opposed to pretending to be drowsy. Compared to the DROZY dataset, our dataset has three advantages: First, we have a substantially larger number of subjects (60 as opposed to 14). Second, for each subject, we have data showing that subject in each of the three predefined alertness classes, whereas in the DROZY dataset some subjects are not recorded in all three states. Third, in DROZY all videos were captured using the same camera position and background, under controlled lab conditions, whereas in our dataset each subject used their own cell phone and a different background. Compared to DROZY, our dataset also has the important difference that it provides color video, whereas DROZY offers several other modalities, but only NIR video.
@cite_8
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Statistics have shown that (20 ) of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable detection. The outputs of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves (73.06 ) detection accuracy on NTHU-drowsy driver detection benchmark dataset. Drowsiness is a major cause of accidents, in particular in road transportation. It is thus crucial to develop robust drowsiness monitoring systems. There is a widespread agreement that the best way to monitor drowsiness is by closely monitoring symptoms of drowsiness that are directly linked to the physiology of an operator such as a driver. The best systems are completely transparent to the operator until the moment he she must react. In transportation, cameras placed in the passenger compartment and looking at least at the face of the driver are most likely the best way to sense physiology related symptoms such as facial expressions and the fine behavior of the eyeballs and eyelids. We present here the new database1 called DROZY that provides multiple modalities of data to tackle the design of drowsiness monitoring systems and related experiments. We also present two novel systems developed using this database that can make predictions about the speed of reaction of an operator by using near-infrared intensity and range images of his her face. In the present study, a vehicle driver drowsiness warning system using image processing technique with fuzzy logic inference is developed and investigated. The principle of the proposed system is based on facial images analysis for warning the driver of drowsiness or inattention to prevent traffic accidents. The facial images of driver are taken by a CCD camera which is installed on the dashboard in front of the driver. A fuzzy logic algorithm and an inference are proposed to determine the level of fatigue by measuring the blinding duration and its frequency, and warn the driver accordingly. The experimental works are carried to evaluate the effect of the proposed system for drowsiness warning under various operation conditions. The experimental results indicated that the proposed expert system is effective for increasing safe in drive. The detail of image processing technique and the characteristic also is present in this paper. Drowsiness detection is vital in preventing traffic accidents. Eye state analysis - detecting whether the eye is open or closed - is critical step for drowsiness detection. In this paper, we propose an easy algorithm for pupil center and iris boundary localization and a new algorithm for eye state analysis, which we incorporate into a four step system for drowsiness detection: face detection, eye detection, eye state analysis, and drowsy decision. This new system requires no training data at any step or special cameras. Our eye detection algorithm uses Eye Map, thus achieving excellent pupil center and iris boundary localization results on the IMM database. Our novel eye state analysis algorithm detects eye state using the saturation (S) channel of the HSV color space. We analyze our eye state analysis algorithm using five video sequences and show superior results compared to the common technique based on distance between eyelids.