Vision-based drowsiness detector for real driving conditions

García, I.; Bronte, Sebastián; Bergasa, Luis M.; Almazán, Javier; Yebes, J. Javier

doi:10.1109/ivs.2012.6232222

Cited by 80 publications

(58 citation statements)

References 13 publications

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“…Most of the methods are initialized by Viola-Jones [17] type algorithm to detect face and eyes [5,13]. Combined with region tracking [12,18] is often preferred to achieve higher detection rate for non-frontal faces.…”

Section: Related Workmentioning

confidence: 99%

“…This method achieves precision of 80% on a dataset (not available) consisting of 10 videos of people while driving a car. The percentage of eye closure for drowsiness detection [13] is calculated from the ratio between the iris height in the frame and the nominal value assigned during a ten-second calibration.…”

Section: Related Workmentioning

confidence: 99%

“…Eye blink detection can be based on motion tracking within the eye region [7]. Some methods try to estimate the state of an eye (open, closed [12] or the eye closure [13]) for individual frames which is consequently used in a sequence for blink detection. Other methods compute a difference between frames (pixels values [14], descriptors [15], etc.).…”

Section: Introductionmentioning

confidence: 99%

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Eye blink detection based on motion vectors analysis

Fogelton

Benešová

2016

Computer Vision and Image Understanding

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Eye blink detection based on motion vectors analysis

Fogelton

Benešová

2016

Computer Vision and Image Understanding

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“…After thorough analysis, it was found that the HAAR training to detect closed eyes wasn't done properly (Garcia et al, 2012). The solution to this was generating an accurate xml file which was used in the program.…”

Section: Resultsmentioning

confidence: 99%

i-Car: An Intelligent and Interactive Interface for Driver Assistance System

Kumar¹,

Ahmed²,

Shetty³

et al. 2014

Sci. Technol. Arts Res. J.

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Article InformationThe aim of the present research was to reduce accidents by assisting the driver in various aspects of driving such as lane detection, pedestrian and car detection, driver drowsiness detection and rear view parking assistance. The methodology combines the computer vision techniques with pattern recognition, feature extraction, machine learning, object recognition, human computer interaction and parallel processing in a nutshell. The proposed system provides robust extraction of lane markings in various types and alerts the driver attempting to drift from the lane. It also detects the pedestrians and cars which are at a vulnerable distance to be hit by the vehicle and alarms the driver well ahead of time. The system uses eye closure based decision algorithm to detect driver drowsiness in all conditions and also warns by interactive voice early enough to avoid the accidents. It also assists the driver while reversing the vehicle, by providing a clear view of his blind spot areas. Computer vision algorithms like Hough's Transform, Canny Edge detection and HAAR classifiers were applied to meet the objectives. The integrated module was analyzed and tested in different terrains and various lighting condition to produce an accurate and robust real-time assistance system (Sivaraman et al., 2014). iCar is an innovative prototype in the Information Technology with minimum hardware like low cost webcams. It emerged as an Interactive Technology with an interactive audio, visual, touch and touch-less interfaces. These can assist to avoid accidents in the world by intelligently ignoring certain hardware sensors like IR, UV, Acoustic, Proximity and mechanical devices like costlier LIDAR (Light Detection and Ranging) fitted in Google Car. Present research findings outperform the state of the art research like CalTech (Aly et al., 1997). Attempts of depth sensing even using Microsoft Kinect could be ignored by the present technology, the iCar.

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“…The work in [9] provides eye detection and head pose estimation; the latest is based on matching of specific features (eye distance) with a model and tracking the movement using optical flow. Other approaches take advantage of the specific features of the IR cameras, on which, due to the specific illumination condition, the pupil can be easily detected; thus, the detection of the eyes is easier [10], [11]. Finally, stereo systems are very useful because they provide 3-D information, but with the disadvantage of the high processing requirements [12]; some of the available commercial systems include these stereo systems [13], [14].…”

Section: State Of the Artmentioning

confidence: 99%

Driver Monitoring Based on Low-Cost 3-D Sensors

Peláez

García

Escalera

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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Abstract-A solution for driver monitoring and event detection based on 3-D information from a range camera is presented. The system combines 2-D and 3-D techniques to provide head pose estimation and regions-ofinterest identification. Based on the captured cloud of 3-D points from the sensor and analyzing the 2-D projection, the points corresponding to the head are determined and extracted for further analysis. Later, head pose estimation with three degrees of freedom (Euler angles) is estimated based on the iterative closest points algorithm. Finally, relevant regions of the face are identified and used for further analysis, e.g., event detection and behavior analysis. The resulting application is a 3-D driver monitoring system based on low-cost sensors. It represents an interesting tool for human factor research studies, allowing automatic study of specific factors and the detection of special event related to the driver, e.g., driver drowsiness, inattention, or head pose.

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Vision-based drowsiness detector for real driving conditions

Cited by 80 publications

References 13 publications

Eye blink detection based on motion vectors analysis

Eye blink detection based on motion vectors analysis

i-Car: An Intelligent and Interactive Interface for Driver Assistance System

Driver Monitoring Based on Low-Cost 3-D Sensors

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