Automatic Fatigue Detection of Drivers through Yawning Analysis

Azim, Tayyaba; Jaffar, M. Arfan; Ramzan, Muhammad; Mirza, Anwar M.

doi:10.1007/978-3-642-10546-3_16

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…In most cases, such methods are capable of recognizing fatigue facial features. However, they may easily fail to detect fatigue driving because of the surrounding context such as sunlight or darkness, and wearable devices [19]. Zhang et al [20] reported that glasses disturbed the detection of eye state.…”

Section: B Facial-feature Based Methodsmentioning

confidence: 99%

Can Steering Wheel Detect Your Driving Fatigue?

Xi²,

Zhang³

et al. 2020

Preprint

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Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the driver's anomalous behaviour is closely monitored by the system. Though many approaches have been proposed for detecting driver fatigue, they largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores about 90%. We also propose promising future directions to deploy this approach in reallife settings, such as applying multimodal learning using several supplementary sensors.

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Section: B Facial-feature Based Methodsmentioning

confidence: 99%

Can Steering Wheel Detect Your Driving Fatigue?

Xi²,

Zhang³

et al. 2020

Preprint

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show abstract

“…(iii) Approaches based on physical signals utilize image processing techniques to measure the driver vigilance level reflected through the driver's face appearance and head/facial feature activity. These techniques are based principally on studying facial features, especially eye state [3][4][5], head pose [6,7], or mouth state [8]. According to the study performed in [9], monitoring driver eye closure and head pose are the most relevant indicators of hypovigilance.…”

Section: Introductionmentioning

confidence: 99%

Driver head pose estimation using efficient descriptor fusion

Alioua

Amine

Rogozan

et al. 2016

J Image Video Proc.

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A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Modelbased approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster and more adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearancebased discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor's features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing'04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

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“…The best performance of the system showed 81.6% accuracy, which was obtained using support vector machines. In another study done by Azim et al , the driver yawning was used for sleepiness detection [ 12 ]. In this study, the Viola-Jones method was used for face recognition, which was previously introduced in [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss

Samiee

Azadi

Kazemi

et al. 2014

Sensors

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This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

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Automatic Fatigue Detection of Drivers through Yawning Analysis

Cited by 13 publications

References 6 publications

Can Steering Wheel Detect Your Driving Fatigue?

Can Steering Wheel Detect Your Driving Fatigue?

Driver head pose estimation using efficient descriptor fusion

Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss

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