Real-Time Vision-Based Driver Drowsiness/Fatigue Detection System

Yao, Kuo Peng; Lin, Wei-Cheng; Fang, Chiung‐Yao; Wang, J. M.; Chang, Shyang-Lih; Chen, S. W.

doi:10.1109/vetecs.2010.5493972

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…Eye movement prediction is practically required for the enhancement of sensor lag. Binary classifications of the eye movements is a broadly studied area relating to works and approaches [18]. Although effective and simple, it causes confusion to the classification of the smooth pursuit.…”

Section: Eye Tracking Methodsmentioning

confidence: 99%

Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter

Asamoah

Oppong

Amoako-Yirenkyi

et al. 2019

AJRCoS

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One of the most promising commercial applications of Human Computer Interface is the vision based Human fatigue detection systems. Most methods and algorithms currently rely heavily on movement of the head and the colorization of the eye ball. In this paper, a new algorithm for detecting human fatigue by relying primarily on eyelid movements as a facial feature is proposed. The features of the eye region and eyelid movement which are geometric in nature are processed alongside each other to determine the level of fatigue of a person. Haar classifiers are employed to detect the eye region and eyelid features. The eye region is, however processed to ascertain attributes of eyelid movement of each individual of interest. The eyelids are then detected as either opened, closed or in transition state. The movement or velocity of the eyelid is tracked using a Kalman filtered velocity function. This algorithm calculates a human blink cycle for each individual, and estimates the associated errors of the eye movement due to friction using the Kalman filter. The study has established human blink cycle calculation as a new classifier to characterize human fatigue and the calculation of the movement of eyelid using the Kalman filter in determining the level of fatigue.

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Section: Eye Tracking Methodsmentioning

confidence: 99%

Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter

Asamoah

Oppong

Amoako-Yirenkyi

et al. 2019

AJRCoS

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“…A study worthy of mention is [12], where several facial features, including those of the eyes, mouth, and gaze, are integrated to measure the driver's vigilance level. These facial features are first located in the input video sequence, then the located facial features are tracked over the subsequent images (facial parameters are estimated during this phase).…”

Section: Detection Of Motor Phenomenamentioning

confidence: 99%

CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy

Bartolini¹,

Luzio²

2021

Computers

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Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.

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“…There are also many researchers from academia that are engaged in the development of DASs. In [ 11 ], authors implement a vision system for monitoring driver’s vigilance by integrating the facial features of the eyes, mouth and head. In et al [ 12 ], authors deal with driver fatigue for which they propose a probabilistic model by considering head, eyelid and gaze movement along with driver’s facial expressions.…”

Section: Background Overview and Related Workmentioning

confidence: 99%

Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs

Bylykbashi

Qafzezi

Ampririt

et al. 2020

Sensors

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The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver’s ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.

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Real-Time Vision-Based Driver Drowsiness/Fatigue Detection System

Cited by 16 publications

References 8 publications

Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter

Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter

CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy

Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs

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