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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Noguchi, Y., Shimada, K., Ohsuga, M., Kamakura, Y., & Inoue, Y. (2009). The assessment of driver’s arousal states from the classification of eye-blink patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5639 LNAI, pp. 414–423). https://doi.org/10.1007/978-3-642-02728-4_44
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