Vision-based drowsiness detector for a realistic driving simulator

García, I.; Bronte, Sebastián; Bergasa, Luis M.; Hernández, Noelia; Delgado, Blanca; Sevillano, M.

doi:10.1109/itsc.2010.5625097

Cited by 27 publications

(18 citation statements)

References 19 publications

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“…Fast processing of data input, great accuracy and sensitivity, and high true detection rates have dramatically increased the reliability of the system and its utilization in automobiles will enhance the performance and accuracy of driver's movement. The comparison of the results and simu lation of algorith ms using some segmentation techniques and Real-Time warning system for driver drowsiness detection methods such as [1 9] [20] [21] [22] and [23] indicated the desirable performance of the system However the databases are different from other methods, but the average performance of our proposed system is better than others. The system proposed in this paper is comprised of three different algorith ms with an acceptable level of performance and an average accuracy of 93.18%.…”

Section: Exprlment Al Resultsmentioning

confidence: 98%

Real-time intelligent alarm system of driver fatigue based on video sequences

Rezaee

Alavi

Madanian

et al. 2013

2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

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Developing intelligent systems to prevent car accidents can be very effective in minimizing accident death toll. One of the factors which play an important role in accidents is the human errors including driving fatigue relying on new smart techniques; this paper detects the signs of fatigue and sleepiness in the face of the person at the time of driving. The proposed system is based on three se parate algorithms. In this model, the person's face is filmed by a camera in the first step by receiving 15fps video sequence. Then, the images are transformed from RGB space into YC bC r and HS V spaces. The face area is separated from other parts and highly accurate HDP is achieved. That the eyes are open or closed in a specific time interval is determined by focusing on thresholding and equations concerning the symmetry of human faces a finally using K-means Clustering, the frequency of yawning is identified. The proposed system has been implemented on four different video sequences with average accuracy of 93.18% and detection rate (DR) of 92.71 % out of total 35000 image frames. High accuracy in segmentation, low error rate and quick processing of input data distinguishes this system from similar ones. This system can minimize the number of accidents caused by drivers' fatigue.

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Section: Exprlment Al Resultsmentioning

confidence: 98%

Real-time intelligent alarm system of driver fatigue based on video sequences

Rezaee

Alavi

Madanian

et al. 2013

2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

View full text Add to dashboard Cite

show abstract

“…Then the Active Shape Model is employed to find totally different facial landmarks on the face image. The utilization of those totally different facial landmarks to align each face images with face mean shape method [24] is done by barycentric coordinate based mapping process. The feature extraction using LOP algorithm in the face elements after the face alignment is obtained and the facial expression is recognized using CNN.…”

Section: Proposed Methodsmentioning

confidence: 99%

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Suchitra*¹,

Priya²,

Poovaraghan³

et al. 2019

IJRTE

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Driver’s inattention is one of the major factors and reasons in occurrence of many road accidents and unforeseen crashes. Hence it is crucial to develop an automatic driver warning system that can send timely warning signals to the drivers. This issue involves determining the driver’s mental state that is ultimately based on the driver’s facial expressions. Automated facial emotion recognition is a recent development in the image processing domain and is the need of the hour in applications like driver warning systems. The existing methods are capable of recognizing facial emotions even when provided with a noisy signal or imperfect data, but ultimately it lacks accuracy. It is also ineffective in dealing with spontaneous emotions, and recognition. The proposed approach develops a driver warning system that extracts the facial expressions based on a novel efficient Local Octal Pattern (LOP) and effectively recognizes the facial expressions based on Deep Neural Networks, Convolutional Neural Networks (CNN). The LOP feature map serves as an input to CNN and guides in the selection of CNN learning data thereby improving and further enhancing the understanding and learning of CNN. It also has an ability to recognize both natural and spontaneous emotions, as well as image and video can be considered as an input.The experimental results consideringYawDD dataset indicates that the proposed system has been efficiently evaluated by considering the with metrics such as Precision, Recall and F-Score and thereby it is observed and inferred that the proposed system obtained a high recall rate of 96.09% in comparison with the other state-of-the-art methods

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“…In general, each weak classifier was based on a very simple visual feature. Such kinds of features are commonly referred as "Haar-like features" [33][34][35].…”

Section: Facial Ir Image Records and Measurementmentioning

confidence: 99%

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

et al. 2019

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Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.

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Vision-based drowsiness detector for a realistic driving simulator

Cited by 27 publications

References 19 publications

Real-time intelligent alarm system of driver fatigue based on video sequences

Real-time intelligent alarm system of driver fatigue based on video sequences

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

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