IEEE Transactions on Pattern Analysis and Machine Intelligence
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications: An International Journal
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To realize the real-time assessment of driver's arousal states, we propose the assessment method based on the analysis of eye-blink characteristics form image sequences. The driver's arousal level while driving is not monotonous falling from high to low. We proposed the two-dimensional arousal states transition model which was taken into account the fact that a driver usually held out against sleepiness. The eye-blink pattern categories were classified from image sequence using HMM (Hidden Markov Model), then the driver's arousal states were finally assessed using HMM by histogram distribution of those typical eye-blink categories. The arousal assessment results are also verified against the rating results by trained raters.