IEEE Access (Jan 2021)
Classification of Eye Movement and Its Application in Driving Based on a Refined Pre-Processing and Machine Learning Algorithm
Abstract
The eyes are the first channel used by humans to obtain various types of visual information from the outside world and, especially when driving, 80-90% of information is received through the eyes. Eye movement behaviors are generally divided into six types, but attention is often paid to fixation, saccade, and smooth pursuit. Due to their importance, it is essential to classify eye movement behaviors accurately. The classification of eye movements should be a complete process, including the three steps of pre-processing, classification, and post-processing. However, it is very uncommon for all of these steps to be included in the eye-tracking literature when eye movement classification is discussed. Therefore, first, this paper proposes a refined eye movement data pre-processing framework and an improved method consisting of three steps is introduced. Second, an eye movement classification algorithm based on an improved decision tree that is independent of the threshold setting and application environment is proposed, and a post-processing consisting of merging adjacent fixations and discarding short fixations is described. Finally, the application of the classified eye movement behavior in the driving field is described, including the estimation of preview time using fixation and the estimation of time-to-collision using smooth pursuit. Two important results are obtained in this paper. One concerns the classification accuracy of eye movement behavior, the F1-scores of fixation, saccade, and smooth pursuit being respectively 92.63%, 93.46%, and 65.29%, which are higher than the scores of other algorithms. The other relates to the application to driving. On the one hand, the preview time calculated by fixation is mostly distributed around 1-6s, which is closer to reality than the traditional setting of 1s. At the same time, the regression relationship between the preview time and the road turning radius is also quantitatively analyzed and their regression function is obtained. On the other hand, the average estimated error of time-to-collision used by smooth pursuit is 7.37%. These results can play an important role in the development of ADAS and the improvement of traffic safety.
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