Purpose-In order to prevent accidents during driving,driver drowsiness detection systems have become a hot topic for researchers.There are various types of features that can be used to detect drowsiness.Detection can ...Purpose-In order to prevent accidents during driving,driver drowsiness detection systems have become a hot topic for researchers.There are various types of features that can be used to detect drowsiness.Detection can be done by utilizing behavioral data,physiological measurements and vehicle-based data.The existing deep convolutional neural network(CNN)models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos.However,the developed model suffered from the limitation of high computational cost because of the application of approximately 140million parameters.Design/methodology/approach-The proposed model uses significant feature parameters from the feature extraction process such as ReliefF,Infinite,Correlation,Term Variance are used for feature selection.The features that are selected are undergone for classification using ensemble classifier.Findings-The output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implications-In this research work higher end camera are required to collect videos as it is cost-effective.Therefore,researches are encouraged to use the existing datasets.Practical implications-This paper overcomes the earlier approach.The developed model used complex deep learning models on small dataset which would also extract additional features,thereby provided a more satisfying result.Originality/value-Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.展开更多
文摘Purpose-In order to prevent accidents during driving,driver drowsiness detection systems have become a hot topic for researchers.There are various types of features that can be used to detect drowsiness.Detection can be done by utilizing behavioral data,physiological measurements and vehicle-based data.The existing deep convolutional neural network(CNN)models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos.However,the developed model suffered from the limitation of high computational cost because of the application of approximately 140million parameters.Design/methodology/approach-The proposed model uses significant feature parameters from the feature extraction process such as ReliefF,Infinite,Correlation,Term Variance are used for feature selection.The features that are selected are undergone for classification using ensemble classifier.Findings-The output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implications-In this research work higher end camera are required to collect videos as it is cost-effective.Therefore,researches are encouraged to use the existing datasets.Practical implications-This paper overcomes the earlier approach.The developed model used complex deep learning models on small dataset which would also extract additional features,thereby provided a more satisfying result.Originality/value-Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.
