Robust driver fatigue recognition using image processing

Ahmed, Rafi; Emon, Kazi Emrul Kayes; Hossain, Md. Foisal

doi:10.1109/iciev.2014.6850713

Cited by 24 publications

(22 citation statements)

References 11 publications

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“…Face Detection Face detection is the first stage. There are numerous challenges that are belongs to the face detection such as position, orientation, lighting conditions, variability of size, shape and color [1]. Generally, camera is fixed at the front of the driver so it is easy for detection of face and head tilt determination.…”

Section: Proposed Systemmentioning

confidence: 99%

“…Another one method is driver performance analysis which is used for monitoring the vehicles on road and lane detection. Non-intrusive methods are helpful to detect facial expressions for fatigue recognition Eye closure and mouth gaping are popular symptoms in the driver fatigue recognition [1]. This is real time system which will consider all fatigue symptoms collectively such as eye closure, head tilting (where head is moving to the left or right) yawning, lanedetection.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Driver Fatigue Recognition Based on Image Processing

Ingale¹,

Wasnik²,

Peddawad³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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Driver fatigue is the main reason of serious damages among all other road accidents. Thus, a new system is proposed with a modern approach which will detect driver fatigue by considering most of the fatigue symptoms such as eye closure, yawning, head tilting. Inattentive vehicle movement on the road under fatigue condition is also considerable for driver fatigue. The approach of this system is to detect these symptoms for the best driving condition on the road. These symptoms are monitored by using two cameras. Thus, a robust system is proposed where head tilting, facial expressions and lane departure for fatigue will be detected collectively. First step of this system is to visualize the face of the driver from one camera while another camera is used to track the road. Then face, eye and mouth (check for yawning) detections of the driver are performed. Facial detection of our method is free from any kind of wrong or bogus detection. Besides, the detection process can strongly determine the desired part of the face in spite of poor light intensity of camera and more realistic to implement. Head tilting detection makes this approach more promising. Lane departure detection in the approach gives this system an advanced level. Lane detection measures the distance of the vehicle between two lanes. Alarm system has been made which will be activated if any fault is found through these detection procedures.

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Section: Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real Time Driver Fatigue Recognition Based on Image Processing

Ingale¹,

Wasnik²,

Peddawad³

et al. 2017

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

show abstract

“…To track the gaze direction of the driver, they traced the center of the pupil using CDF analysis and estimated the frequency of eye-movement. Rafi Ahmed et al [4] described a modern approach which detects driver's fatigue symptoms, eye closure, yawning, and head tilting are included. M. Omidyeganeh et al [5] presented a robust and intelligent scheme for driver drowsiness detection employing the fusion of eye closure and yawning detection methods.…”

Section: A Fatigue Behaviormentioning

confidence: 99%

Design and implementation of a dangerous driving behavior analysis system

2016

2016 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)

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Dangerous driving behavior is the major factor to cause most of driving accidents. We present a novel method for dangerous driving behavior analysis. When the dangerous driving is detected, the system will issue an alert message to remind driver to pay more attention. The proposed system can detect three kinds of dangerous behavior: dozing, eating, and phoning. Furthermore, since we propose a novel algorithm to adaptively detect skin color, the proposed system can run in various light conditions with good quality. The proposed algorithm is verified and implemented on an in-vehicle embedded system -TREK-668. The performance on PC can reach 150 fps in average on input video with QVGA (320×240) resolution. On embedded systems, the proposed system can achieve 24fps in average on input video with QVGA (320×240) resolution.I.

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“…The driver fatigue is determined by the frequency of eye blinking and head tilting and the drowsiness level is calculated by Raspberry Pi3 along with a Pi camera [8]. The experiment is carried out on ten volunteers under different lighting conditions and they obtain face and eye detection rate up to 99.59% [7]. Real-time video-based vision processing method is recommended to estimate fatigue of the driver and a buzzer is used to alert the driver [11].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Detection Using Raspberry Pi 3

Chellappa¹,

Reddy²,

Ezhilarasie³

et al. 2018

IJET

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Driver drowsiness is a primary cause of several highway calamities leads to severe physical injuries, loss of money, and loss of human life. The implementation of driver drowsiness detection in real-time will aid in avoiding major accidents. The system is designed for four-wheelers wherein the driver's fatigue or drowsiness is detected and alerts the person. The proposed method will use 5-megapixel Raspbian camera that captures driver's face and eyes and processes the images to detect driver's fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method constitutes of various stages to determine wakefulness of the driver. According to this output, the warning message is generated. Haar Cascade Classifiers is used to detect the blink duration of the driver and Eye Aspect Ratio (EAR) is calculated. Finally, the alert message along with car plate number is sent to the concerned person mobile with help of Ubidots cloud service and Twilio API. For this Raspberry Pi 3 with Raspbian (Linux Based) Operating System is used.

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Robust driver fatigue recognition using image processing

Cited by 24 publications

References 11 publications

Real Time Driver Fatigue Recognition Based on Image Processing

Real Time Driver Fatigue Recognition Based on Image Processing

Design and implementation of a dangerous driving behavior analysis system

Fatigue Detection Using Raspberry Pi 3

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