Eye Movement State Trajectory Estimator based on Ancestor Sampling

Abstract

Human gaze dynamics mainly concern about the sequence of the occurrence of three eye movements: fixations, saccades, and microsaccades. In this paper, we correlate them as three different states to velocities of eye movements. We build a state trajectory estimator based on ancestor sampling (ST EAS) model, which captures the features of the human temporal gaze pattern to identify the kind of visual stimuli. We used a gaze dataset of 72 viewers watching 60 video clips which are equally split into four visual categories. Uniformly sampled velocity vectors from the training set, are used to find the best suitable parameters of the proposed statistical model. Then, the optimized model is used for both gaze data classification and video retrieval on the test set. We observed 93.265% of classification accuracy and a mean reciprocal rank of 0.888 for video retrieval on the test set. Hence, this model can be used for viewer independent video indexing for providing viewers an easier way to navigate through the contents.