Driver-Independent Assessment of Arousal States from Video Sequences Based on the Classification of Eyeblink Patterns

Abstract

In this paper, we propose a novel approach to assess driver's arousal states based on the analysis of eyeblink characteristics. We focus on a non-intrusive and driver-independent system. We use Hidden Markov Models (HMMs) to classify eyeblink patterns from the video of the drivers, and the arousal states are estimated from the histogram variations of these typical blink patterns. A strong correlation between the eyeblink patterns derived from this approach and those derived from the recorded EOG (electro-occulography) waveforms can be observed. The arousal assessment results are also verified against the rating results by a trained rater.