Driver Drowsiness Detection Based on Face Feature and PERCLOS

Junaedi, Suhandi; Akbar, Habibullah

doi:10.1088/1742-6596/1090/1/012037

Cited by 35 publications

(18 citation statements)

References 11 publications

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“…In order to adapt this method to field studies, we optimized the method with improved robustness, employed a parallel computing mechanism, and used the P70 index as a metric in this study. P70 is defined as the duration when the eyes are closed at least 70%, often employed as a fatigue metric when analyzing PERCLOS data [25]. PERCLOS values, such as P70, increase when subjects become fatigued [25].…”

Section: Selection Of Fatigue Detection Methodsmentioning

confidence: 99%

A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection

Zhang

Chen

Liu

et al. 2021

IJERPH

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The fatigue of air traffic controllers (ATCOs) on duty seriously threatens air traffic safety and needs to be managed. ATCOs perform several different types of work, with each type of work having different characteristics. Nonetheless, the influence of work type on an ATCO’s fatigue has yet to be demonstrated. Here, we present a field study in which the fatigue of ATCOs working in two types of work was compared based on an optimized data-driven method that was employed to detect the percentage of eyelid closure over the pupil over time (PERCLOS). Sixty-seven ATCOs working within two typical jobs (i.e., from the terminal control unit (TCU) and area control unit (ACU)) were recruited, and their fatigue was detected immediately before and after shift work using PERCLOS. Using a Spearman correlation test analysis, the results showed that the influence of work type on an ATCO’s fatigue had interesting trends. Specifically, the ATCOs at the TCU who handle departures and arrivals, which include converging with and maneuvering around conflicts, retain normal circadian rhythms. Their fatigue was significantly influenced by the various demands from tasks focusing on sequencing and conflict resolution and by the time phase of a normal circadian rhythm. At the ACU, ATCOs manage flights that are mainly on route, causing monotonous monitoring and routine reporting tasks, and the ATCOs generally have frequent night shifts to handle overflights. Their fatigue was significantly influenced by the demand characteristics from tasks, but changes in fatigue rule were not consistent with a normal circadian rhythm, revealing that the ATCOs’ circadian rhythms may have already been slightly disturbed. Furthermore, the interactions between task demand and circadian rhythm with an ATCO’s fatigue were significantly observed in ATCOs working in the TCU but not in those in the ACU. This study provides first evidence that an ATCO’s work type influences his or her fatigue. This discovery may incite stakeholders to consider work type in the management of employee fatigue, not only in the civil aviation industry but also in other transport industries.

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Section: Selection Of Fatigue Detection Methodsmentioning

confidence: 99%

A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection

Zhang

Chen

Liu

et al. 2021

IJERPH

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“…Junaedi and Akbar calculated PERCLOS by detecting the eyes and used it to judge the fatigue. ey used the YawDD dataset [11]. Savas and Becerikli tried to use the SVM algorithm to detect driver fatigue.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

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“…The PERCLOS (Percentage of Eyelid Closure Over the Pupil Over Time) refers to the percentage of time when eyes are closed for a certain time. The PERCLOS is currently the most effective indicator for detecting driver sleepiness based on eye data (Junaedi & Akbar, 2018). Deng and Wu (2019) accurately judged the driver's fatigue state by analyzing the state of the driver's eyes and mouth.…”

Section: Index Selectionmentioning

confidence: 99%

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Wei

Feng

Zhao

et al. 2021

BJRBE

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Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.

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Driver Drowsiness Detection Based on Face Feature and PERCLOS

Cited by 35 publications

References 11 publications

A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection

A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

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