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

Abstract

With recent advances in image recognition, the assessment of driver's arousal level using blinking image sequences has been expected. In this paper, we demonstrated the possibility of assessing driver's arousal level by analyzing blinking image sequences. We focused on some typical blink waveform patterns occurred under drowsy condition. We used the results of EOG (Electro-occulogram) waveform clustering as the baseline for HMM (Hidden Markov Model) blinking labeling due to the difficulty of defining blinking labels from blinking image sequence. The blink pattern classes were classified by using the HMMs based on blinking image sequences. The driver's arousal level was finally estimated by histogram variation per minute of those typical blink classes.