Nonintrusive Driver Fatigue Detection

Fan, Xiao; Yin, Baocai; Sun, Yanfeng

doi:10.1109/icnsc.2008.4525345

Cited by 22 publications

(7 citation statements)

References 10 publications

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“…Rong-Ben et al 7 extract the texture features of the eye region by Gabor wavelet transformation and classify driver fatigue by using a neural network. Fan et al 8 select the most discriminative features extracted by local binary pattern (LBP) features of eye areas via AdaBoost to detect drowsiness. Kim et al 9 evaluate the states of the eyes using features of CMYK and HSI color spaces.…”

Section: Relative Workmentioning

confidence: 99%

Semi-cascade network for driver’s distraction recognition

Liu

Kang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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A novel method for the eight most common driver’s distraction actions recognition is presented in this paper. To this end, a semi-cascade network (SCN) with very lightweight architecture is designed. The approach recognizes the morphology of the human face and hands to make judgments about the driver’s actions rather than just judging facial information. In order to subdivide similar actions, a SCN structure which effectively reduces the network’s scale is employed. A joint training approach is proposed for training the network and achieving 95.61% accuracy. In addition, to verify the validity of the method, a dataset containing 100,000 samples is created. Finally, a warning strategy is provided for our system and 93.9% warning rate for the driver’s distraction behavior is achieved.

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Section: Relative Workmentioning

confidence: 99%

Semi-cascade network for driver’s distraction recognition

Liu

Kang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…10,13,19,29,35,37 The comparison of the approaches is not easy, because results are reported in different nonstandard ways. 5,8,9,15,34 For a comprehensive survey on eye and gaze tracking models and approaches see ref. 8,27 However, it is possible to say that approaches based on color analysis are limited by illumination conditions and thus cannot be employed at night.…”

Section: State Of the Artmentioning

confidence: 99%

Face salient points and eyes tracking for robust drowsiness detection

Jimenez-Pinto¹,

Torres-Torriti²

2011

Robotica

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Link to this article: http://journals.cambridge.org/abstract_S0263574711000749How to cite this article: J. Jimenez-Pinto and M. Torres-Torriti (2012). Face salient points and eyes tracking for robust drowsiness detection. SUMMARYMeasuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, and eyebrow rising. To overcome these challenges, our approach combines the well-known Viola-Jones face detector with the motion analysis of Shi-Tomasi salient features within the face. The location of the eyes and blinking is important to refine the tracking of the driver's head and compute the so-called PERCLOS, which is the percentage of time the eyes are closed over a given time interval. The latter cue is essential for noninvasive driver's alert state estimation as it has a high correlation with drowsiness. To further improve the location of the eyes under different conditions of illumination, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. The paper shows that motion analysis of the salient points, in particular cluster mass centers and spatial distributions, yields better head tracking results compared to the state-ofthe-art and provides measures of the driver's alert state.

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“…Physiological indicators are considered to be the best measurements for driving fatigue and include electroencephalogram (EEG) [19], electrocardiogram (ECG) [20], electromyography (EMG) [21], heart rate [22], eye gaze distribution [9], saccade range, and blink frequency [23]. As drivers typically do not wear sensors, driving behaviors, such as eye closure duration [24] and yawning frequency [25], can only be extracted from videos captured by monitoring cameras. Researchers must estimate driving fatigue status based on these driving behaviors.…”

Section: Introductionmentioning

confidence: 99%

Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change

Wang

Chen

Zhu

et al. 2019

Sensors

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Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention.

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Nonintrusive Driver Fatigue Detection

Cited by 22 publications

References 10 publications

Semi-cascade network for driver’s distraction recognition

Semi-cascade network for driver’s distraction recognition

Face salient points and eyes tracking for robust drowsiness detection

Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change

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