Sleepy Eye's Recognition for Drowsiness Detection

Lin, Shinfeng D.; Lin, Jia-Jen; Chung, Chin-Yao

doi:10.1109/isbast.2013.31

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…Non-intrusive systems primarily monitor driver fatigue by extracting driver's facial features and operational behavior characteristics. [7][8][9][10][11] During the monitoring process, the driver's normal driving will not be disturbed, and the data acquisition and fatigue monitoring can objectively reflect the driver's actual operating behavior or state. 11 At present, the non-intrusive method has become popular in the field of driver fatigue monitoring.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data

Yang

Chen

et al. 2019

International Journal of Distributed Sensor Networks

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The study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions.

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Section: Introductionmentioning

confidence: 99%

A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data

Yang

Chen

et al. 2019

International Journal of Distributed Sensor Networks

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“…The yawn detection is carried out by Histogram of Oriented (HOG) and calculate the scale and vector of each pixel. S. D. Lin et al [2] presents the statistics of the car accidents that happen frequently due to drowsiness of the car driver. This method basically follows four main steps.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness detection using Eye-Blink frequency and Yawn count for Driver Alert

2019

IJITEE

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Several reasons can be sighted for the cause of these road accidents. Few of them include lack of sleep, drunk driving, violation of traffic rules, etc. Amongst them, the state of drowsiness and drunk driving alone contributes to 36% of accidents. Though a number of national schemes and traffic rules have been implemented to avoid these road accidents, it could only bring down the accident rate by 10%. As car accidents are one of the major issues of concern, this paper will be discussing mainly on Drunk driving or drowsiness. In these recent years, various methods have been proposed to implement drowsiness detection based on Hough transforms. Here, in this paper, we have determined a technique to detect drowsiness among car drivers and alert them whenever they tend to sleep. The algorithm is based on eye-blink and yawn frequency. It deals with an eye blink yawn frequency algorithm that uses eye coordinates to keep track of person and determine the open or closed state of the eye and generate an alarm if the driver is drowsy. The yawn count is determined by checking the frequency of yawn count with a minimum threshold value.

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