Feature Selection for Driver Drowsiness Detection

Panda, Santosh Kumar; Kolhekar, Megha

doi:10.1007/978-981-13-6459-4_14

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Besides parameters of eye behavior currently connected with fatigue analysis, the driver emotional state (including stress and fatigue) could be also estimated by analysis of some selected characteristic points on the face (eye corners, mouth corners or eyebrows). Panda et al [175] described feature selection for analysis of driver drowsiness using eye movement to detect eye opening. Among evaluated methods (Canny Edge, Local Binary Patterns, Gabor Filter Bank, etc.…”

Section: ) Facial Expressionmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.

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Section: ) Facial Expressionmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…The formula for negative class precisions is presented in (13). (12) (13) d) F1-Score: The F1-Score represents a weighted average between precision and recall and is hence considered the most appropriate measure of model performance in some literature [57,61]. Equation (14) presents the mathematical formula to calculate F1-Score [61,62].…”

Section: ) Model Evaluationmentioning

confidence: 99%

Data Augmentation for Deep Learning Algorithms that Perform Driver Drowsiness Detection

Mohamed¹,

Patel²,

Naicker³

2023

IJACSA

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Driver drowsiness is one of the main causes of driver-related motor vehicle collisions, as this impairs a person's concentration whilst driving. With the enhancements of computer vision and deep learning (DL), driver drowsiness detection systems have been developed previously, in an attempt to improve road safety. These systems experienced performance degradation under real-world testing due to factors such as driver movement and poor lighting. This study proposed to improve the training of DL models for driver drowsiness detection by applying data augmentation (DA) techniques that model these real-world scenarios. This paper studies six DL models for driver drowsiness detection: four configurations of a Convolutional Neural Network (CNN), two custom configurations as well as the architectures designed by the Visual Geometry Group (VGG) (i.e. VGG16 and VGG19); a Generative Adversarial Network (GAN) and a Multi-Layer Perceptron (MLP). These DL models were trained using two datasets of eye images, where the state of eye (open or closed) is used in determining driver drowsiness. The performance of the DL models was measured with respect to accuracy, F1-Score, precision, negative class precision, recall and specificity. When comparing the performance of DL models trained on datasets with and without DA in aggregation, it was found that all metrics were improved. After removing outliers from the results, it was found that the average improvement in both accuracy and F1 score due to DA was +4.3%. Furthermore, it is shown that the extent to which the DA techniques improve DL model performance is correlated with the inherent model performance. For DL models with accuracy and F1-Score ≤ 90%, results show that the DA techniques studied should improve performance by at least +5%.

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Temporal and spatial feature based approaches in drowsiness detection using deep learning technique

Pandey

Muppalaneni

2021

J Real-Time Image Proc

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Feature Selection for Driver Drowsiness Detection

Cited by 4 publications

References 11 publications

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Data Augmentation for Deep Learning Algorithms that Perform Driver Drowsiness Detection

Temporal and spatial feature based approaches in drowsiness detection using deep learning technique

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