Driver distraction analysis using face pose cues

Hari, Ch. V. M. K.; Sankaran, Praveen

doi:10.1016/j.eswa.2021.115036

Cited by 16 publications

(4 citation statements)

References 63 publications

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“…However, their model outperformed them all with a detection accuracy of 79.84%. Furthermore, Hari C.V. and Praveen Sankaran [23] proposed a two-layer cluster approach with Gabor features and SVM for classification and achieved an accuracy of 95.8%. This approach was compared with deep learning-constructed CNN.…”

Section: Machine Learningmentioning

confidence: 99%

“…Although the eye state is a measure that is widely used by researchers [17][18][19][20], it is highly sensitive to light and glasses. Other facial measures are yawning [20][21][22] and head position [23,24]. In addition, behavioral characteristics can be fused with vehicle measures, such as the steering wheel angle and grip, lane monitoring and speed [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

2022

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Modern cities have imposed a fast-paced lifestyle where more drivers on the road suffer from fatigue and sleep deprivation. Consequently, road accidents have increased, becoming one of the leading causes of injuries and death among young adults and children. These accidents can be prevented if fatigue symptoms are diagnosed and detected sufficiently early. For this reason, we propose and compare two AlexNet CNN-based models to detect drivers’ fatigue behaviors, relying on head position and mouth movements as behavioral measures. We used two different approaches. The first approach is transfer learning, specifically, fine-tuning AlexNet, which allowed us to take advantage of what the model had already learned without developing it from scratch. The newly trained model was able to predict drivers’ drowsiness behaviors. The second approach is the use of AlexNet to extract features by training the top layers of the network. These features were reduced using non-negative matrix factorization (NMF) and classified with a support vector machine (SVM) classifier. The experiments showed that our proposed transfer learning model achieved an accuracy of 95.7%, while the feature extraction SVM-based model performed better, with an accuracy of 99.65%. Both models were trained on a simulated NTHU Driver Drowsiness Detection dataset.

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Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

2022

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“…A similar idea was proposed in [137], [138], which extracted the head pose using a coordinate-pair-angle-method (CPAM) and then DNN for further classification. Praveen et al [226] demonstrated the feasibility of distraction detection by tracking the face pose using a clustered approach based on Gabor features. The full-scale information of a human body was leveraged by an ensemble of ResNets in [227] to distinguish distraction from images of normal driving, yielding an accuracy of 94.28% on the American University in Cairo (AUC) dataset.…”

Section: B Distraction Detection On Human Sensingmentioning

confidence: 99%

In-Vehicle Sensing for Smart Cars

Zeng

Wang

Wang³

et al. 2022

IEEE Open J. Veh. Technol.

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“…To improve road safety, various in-vehicle sensing technologies have been employed to detect driver drowsiness in real-time [ 8 , 9 , 10 ]. Most notably, researchers have frequently resorted to camera-based solutions that rely on various visual features of the eye, mouth, head, or body to assess driver states, e.g., fatigue or distraction [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Wearable System for Driver Drowsiness Detection

Zhang

Zhu

et al. 2023

Sensors

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Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.

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Driver distraction analysis using face pose cues

Cited by 16 publications

References 63 publications

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

In-Vehicle Sensing for Smart Cars

A CNN-Based Wearable System for Driver Drowsiness Detection

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