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.
Highlights
The eyes are the first channel used by humans to obtain various types of visual information from the outside world, and they represent an important way to search for and receive information
Based on the above problems, we propose a refined eye movement data preprocessing framework and an eye movement classification algorithm that is independent of the threshold setting and application environment
The classification of eye movement behavior has an influence on the accuracy of the research results of human visual characteristics
Summary
The eyes are the first channel used by humans to obtain various types of visual information from the outside world, and they represent an important way to search for and receive information. A threshold-based algorithm takes eye movement behavior characteristic parameters (such as the speed or spatial dispersion of eye movement data) as the threshold, which are used to generate classification results. Probability-based algorithms are used to build a characteristic probability distribution model for each kind of eye movement state (for example, the distribution of speed), estimate the posterior probability by using the prior probability, and recalculate the probability distribution parameters to minimize, and obtain classification results. The purpose of the AFKF algorithm is firstly to distinguish saccades from other eye movements by using a Kalman filter and chi-squared test and to classify fixation and smooth pursuit points by using velocity and time thresholds. Based on the above problems, we propose a refined eye movement data preprocessing framework and an eye movement classification algorithm that is independent of the threshold setting and application environment. The fixation and smoothing pursuit points are used to estimate the preview time and collision time while driving
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