Classification of Blink Waveforms Towards the Assessment of Driver’s Arousal Level - An Approach for HMM Based Classification from Blinking Video Sequence

Noguchi, Yoshihiro; Nopsuwanchai, Roongroj; Ohsuga, Mieko; Kamakura, Yoshiyuki

doi:10.1007/978-3-540-73331-7_85

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…Online blink detection with computer vision Many recent computer vision studies have described different algorithms for online eyeblink detection, wherein eyeblinks are identified from a given video stream coming from a standard camera by means of image recognition (Chau & Betke, 2005;Cohn, Xiao, Moriyama, Ambadar, & Kanade, 2003;Kawato & Tetsutani, 2004;Lalonde, Byrns, Gagnon, Teasdale, & Laurendeau, 2007;Morris, Blenkhorn, & Zaidi, 2002;Noguchi, Nopsuwanchai, Ohsuga, & Kamakura, 2007;Ohno, Mukawa, & Kawato, 2003;Smith, Shah, & da Vitoria Lobo, 2003;Sukno, Pavani, Butakoff, & Frangi, 2009); this solution has the advantage of being highly unobtrusive, and blinkstogether with other facial features-can be tracked in real time. Sukno et al tested their algorithm by comparison with ground truth obtained from manual annotations of 861 blinks, which were visually inspected by the authors.…”

Section: Contact-free Recordingmentioning

confidence: 99%

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

et al. 2011

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Event detection is the conversion of raw eyetracking data into events-such as fixations, saccades, glissades, blinks, and so forth-that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols. I-AC works by first correcting blink-related artifacts within pupil diameter values and then estimating blink onset and offset. Artifact correction is achieved with data-driven thresholds, and more reliable pupil data are output. Blink parameters are defined according to previous studies on blink-related visual suppression. Blink detection performance was tested with experimental data by visually checking the actual correspondence between I-AC output and participants' eye images, recorded by the eyetracker simultaneously with gaze data. Results showed a 97% correct detection percentage.

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Section: Contact-free Recordingmentioning

confidence: 99%

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

et al. 2011

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“…India is one of the largest contributors to this number. Today there are many numbers of technologies developed for fatigue monitoring [3]- [16]. The drowsy state detection system can be classified into three kinds.…”

Section: Introductionmentioning

confidence: 99%

An improved fatigue detection system based on behavioral characteristics of driver

Gupta

Aman

Shiva

et al. 2017

2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

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In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.

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“…These techniques attempt to copy the expert doctor decisions mechanism by merging several blinking features on a fixed time-window. Thus, techniques such as Hidden Markov Model (Noguchi et al (2007)), clustering k-mean (Ohsuga et al (2007)), multiple regression analysis (Omi et al (2008)), Fuzzy Expert System (Damousis et al (2009)) and Support Vec-tor Machines (Hu and Zheng (2009)) have been recently explored to monitor drowsiness.…”

Section: Introductionmentioning

confidence: 99%

EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers

Picot

Charbonnier

Caplier

2011

IFAC Proceedings Volumes

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Classification of Blink Waveforms Towards the Assessment of Driver’s Arousal Level - An Approach for HMM Based Classification from Blinking Video Sequence

Cited by 9 publications

References 3 publications

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

An improved fatigue detection system based on behavioral characteristics of driver

EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers

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