  In this work, we tackle the problem of ternary eye movement classification,
which aims to separate fixations, saccades and smooth pursuits from the raw eye
positional data. The efficient classification of these different types of eye
movements helps to better analyze and utilize the eye tracking data. Different
from the existing methods that detect eye movement by several pre-defined
threshold values, we propose a hierarchical Hidden Markov Model (HMM)
statistical algorithm for detecting fixations, saccades and smooth pursuits.
The proposed algorithm leverages different features from the recorded raw eye
tracking data with a hierarchical classification strategy, separating one type
of eye movement each time. Experimental results demonstrate the effectiveness
and robustness of the proposed method by achieving competitive or better
performance compared to the state-of-the-art methods.
