The applicability of probabilistic methods to the online recognition of fixations and saccades in dynamic scenes

Abstract

In many applications involving scanpath analysis, especially when dynamic scenes are viewed, consecutive fixations and saccades, have to be identified and extracted from raw eye-tracking data in an online fashion. Since probabilistic methods can adapt not only to the individual viewing behavior, but also to changes in the scene, they are best suited for such tasks. In this paper we analyze the applicability of two types of main-stream probabilistic models to the identification of fixations and saccades in dynamic scenes: (1) Hidden Markov Models and (2) Bayesian Online Mixture Models. We analyze and compare the classification performance of the models on eye-tracking data collected during real-world driving experiments.